Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [35]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading celeba: 0.00B [00:00, ?B/s]
Found mnist Data
Downloading celeba: 1.44GB [02:38, 9.08MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f88ea0224a8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [36]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[36]:
<matplotlib.image.AxesImage at 0x7f88670c2cf8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [120]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    input_real = tf.placeholder(tf.float32, (None, image_height, image_width, image_channels), name="input_real")
    input_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
    learning_rate = tf.placeholder(tf.float32, (None), name="learning_rate")
    
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [164]:
def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
#         h1 = tf.reshape(images, (-1, 28*28*int(images.shape[-1])))
        
#         h1 = tf.layers.dense(h1, 1024, activation=None)
#         h1 = tf.maximum(alpha * h1, h1)
        
#         h2 = tf.layers.dense(h1, 512, activation=None)
#         h2 = tf.maximum(alpha * h2, h2)
        
#         logits = tf.layers.dense(h2, 1, activation=None)
#         out = tf.sigmoid(logits)
        
#         return out, logits

        size = 256
    
        x1 = tf.layers.conv2d(images, size/4, 5, strides=2, padding='same')
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(x1, size/2, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d(x2, size, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha * x3, x3)
    
        x3f = tf.reshape(x3, (-1, 4*4*size))
        
        logits = tf.layers.dense(x3f, 1)
        output = tf.sigmoid(logits)
        
        return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [165]:
def generator(z, out_channel_dim, is_train=True, alpha=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=(not is_train)):
#         h1 = tf.layers.dense(z, 256, activation=None)
#         h1 = tf.maximum(alpha * h1, h1)
        
#         h2 = tf.layers.dense(h1, 512, activation=None)
#         h2 = tf.maximum(alpha * h2, h2)
        
#         # Logits and tanh output
#         logits = tf.layers.dense(h2, 28*28*out_channel_dim, activation=None)
#         out = tf.tanh(logits)
        
#         return tf.reshape(out, (-1, 28, 28, out_channel_dim))

        size = 256

        x1 = tf.layers.dense(z, 7*7*size)
        
        x1 = tf.reshape(x1, (-1, 7, 7, size))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, int(size/2), 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, int(size/4), 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1s, padding='same')
        output = tf.tanh(logits) * 0.5
        
        return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [166]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.01):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
    
    d_model_real, d_logits_real = discriminator(input_real, reuse=False, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=tf.ones_like(d_model_real) * (1 - 0.9)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [167]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, 
                                             beta1=beta1).minimize(d_loss, 
                                                                   var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, 
                                             beta1=beta1).minimize(g_loss, 
                                                                   var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [168]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.shape[-1]
    example_z = np.random.uniform(-1, 1, size=(n_images, z_dim))

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [177]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    losses = []
    batch_i = 0
    
    batches_n = int(data_shape[0] / batch_size) * epoch_count
    
    input_real, input_z, learn_rate = model_inputs(data_shape[2], data_shape[1], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=0.01)
        
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_i = batch_i + 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learn_rate: learning_rate})

                if batch_i % 1 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{} - ".format(epoch_i + 1, epochs),
                          "Batch {}/{} - ".format(batch_i, batches_n),
                          "Discriminator loss: {:.4f} - ".format(train_loss_d),
                          "Generator loss: {:.4f} - ".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if batch_i % 20 == 0:
                    show_generator_output(sess, 16, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [179]:
batch_size = 128
z_dim = 128
learning_rate = 0.0002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 -  Batch 1/936 -  Discriminator loss: 1.0075 -  Generator loss: 1.0218 - 
Epoch 1/2 -  Batch 2/936 -  Discriminator loss: 0.7437 -  Generator loss: 2.1129 - 
Epoch 1/2 -  Batch 3/936 -  Discriminator loss: 0.6531 -  Generator loss: 2.4898 - 
Epoch 1/2 -  Batch 4/936 -  Discriminator loss: 0.5533 -  Generator loss: 2.4544 - 
Epoch 1/2 -  Batch 5/936 -  Discriminator loss: 0.4594 -  Generator loss: 2.5052 - 
Epoch 1/2 -  Batch 6/936 -  Discriminator loss: 0.4214 -  Generator loss: 2.8483 - 
Epoch 1/2 -  Batch 7/936 -  Discriminator loss: 0.3889 -  Generator loss: 3.2375 - 
Epoch 1/2 -  Batch 8/936 -  Discriminator loss: 0.3902 -  Generator loss: 3.1998 - 
Epoch 1/2 -  Batch 9/936 -  Discriminator loss: 0.3907 -  Generator loss: 3.1861 - 
Epoch 1/2 -  Batch 10/936 -  Discriminator loss: 0.3710 -  Generator loss: 3.5652 - 
Epoch 1/2 -  Batch 11/936 -  Discriminator loss: 0.3606 -  Generator loss: 3.8523 - 
Epoch 1/2 -  Batch 12/936 -  Discriminator loss: 0.3645 -  Generator loss: 3.7731 - 
Epoch 1/2 -  Batch 13/936 -  Discriminator loss: 0.3661 -  Generator loss: 3.6052 - 
Epoch 1/2 -  Batch 14/936 -  Discriminator loss: 0.3587 -  Generator loss: 3.9719 - 
Epoch 1/2 -  Batch 15/936 -  Discriminator loss: 0.3528 -  Generator loss: 4.0753 - 
Epoch 1/2 -  Batch 16/936 -  Discriminator loss: 0.3531 -  Generator loss: 4.2390 - 
Epoch 1/2 -  Batch 17/936 -  Discriminator loss: 0.3905 -  Generator loss: 3.0602 - 
Epoch 1/2 -  Batch 18/936 -  Discriminator loss: 0.3712 -  Generator loss: 3.6028 - 
Epoch 1/2 -  Batch 19/936 -  Discriminator loss: 0.3626 -  Generator loss: 3.6695 - 
Epoch 1/2 -  Batch 20/936 -  Discriminator loss: 0.3673 -  Generator loss: 3.5983 - 
Epoch 1/2 -  Batch 21/936 -  Discriminator loss: 0.3841 -  Generator loss: 3.1868 - 
Epoch 1/2 -  Batch 22/936 -  Discriminator loss: 0.4002 -  Generator loss: 3.0315 - 
Epoch 1/2 -  Batch 23/936 -  Discriminator loss: 0.4024 -  Generator loss: 2.9280 - 
Epoch 1/2 -  Batch 24/936 -  Discriminator loss: 0.3795 -  Generator loss: 3.4183 - 
Epoch 1/2 -  Batch 25/936 -  Discriminator loss: 0.3897 -  Generator loss: 3.1469 - 
Epoch 1/2 -  Batch 26/936 -  Discriminator loss: 0.3929 -  Generator loss: 3.2482 - 
Epoch 1/2 -  Batch 27/936 -  Discriminator loss: 0.4165 -  Generator loss: 2.7197 - 
Epoch 1/2 -  Batch 28/936 -  Discriminator loss: 0.3933 -  Generator loss: 3.7229 - 
Epoch 1/2 -  Batch 29/936 -  Discriminator loss: 0.4396 -  Generator loss: 2.5654 - 
Epoch 1/2 -  Batch 30/936 -  Discriminator loss: 0.4144 -  Generator loss: 4.5094 - 
Epoch 1/2 -  Batch 31/936 -  Discriminator loss: 0.4207 -  Generator loss: 2.7501 - 
Epoch 1/2 -  Batch 32/936 -  Discriminator loss: 0.4097 -  Generator loss: 4.1321 - 
Epoch 1/2 -  Batch 33/936 -  Discriminator loss: 0.4196 -  Generator loss: 2.7710 - 
Epoch 1/2 -  Batch 34/936 -  Discriminator loss: 0.4089 -  Generator loss: 4.1727 - 
Epoch 1/2 -  Batch 35/936 -  Discriminator loss: 0.4376 -  Generator loss: 2.5828 - 
Epoch 1/2 -  Batch 36/936 -  Discriminator loss: 0.4172 -  Generator loss: 4.2539 - 
Epoch 1/2 -  Batch 37/936 -  Discriminator loss: 0.4286 -  Generator loss: 2.6158 - 
Epoch 1/2 -  Batch 38/936 -  Discriminator loss: 0.4019 -  Generator loss: 4.3420 - 
Epoch 1/2 -  Batch 39/936 -  Discriminator loss: 0.4088 -  Generator loss: 2.8260 - 
Epoch 1/2 -  Batch 40/936 -  Discriminator loss: 0.4024 -  Generator loss: 4.5988 - 
Epoch 1/2 -  Batch 41/936 -  Discriminator loss: 0.4157 -  Generator loss: 2.7238 - 
Epoch 1/2 -  Batch 42/936 -  Discriminator loss: 0.3943 -  Generator loss: 4.3047 - 
Epoch 1/2 -  Batch 43/936 -  Discriminator loss: 0.4148 -  Generator loss: 2.7464 - 
Epoch 1/2 -  Batch 44/936 -  Discriminator loss: 0.4345 -  Generator loss: 5.0531 - 
Epoch 1/2 -  Batch 45/936 -  Discriminator loss: 0.4034 -  Generator loss: 2.8524 - 
Epoch 1/2 -  Batch 46/936 -  Discriminator loss: 0.3862 -  Generator loss: 3.5644 - 
Epoch 1/2 -  Batch 47/936 -  Discriminator loss: 0.3879 -  Generator loss: 3.1393 - 
Epoch 1/2 -  Batch 48/936 -  Discriminator loss: 0.3830 -  Generator loss: 3.5370 - 
Epoch 1/2 -  Batch 49/936 -  Discriminator loss: 0.3849 -  Generator loss: 3.2536 - 
Epoch 1/2 -  Batch 50/936 -  Discriminator loss: 0.3792 -  Generator loss: 3.6464 - 
Epoch 1/2 -  Batch 51/936 -  Discriminator loss: 0.3898 -  Generator loss: 3.0747 - 
Epoch 1/2 -  Batch 52/936 -  Discriminator loss: 0.3937 -  Generator loss: 4.1970 - 
Epoch 1/2 -  Batch 53/936 -  Discriminator loss: 0.4234 -  Generator loss: 2.6957 - 
Epoch 1/2 -  Batch 54/936 -  Discriminator loss: 0.4404 -  Generator loss: 4.8511 - 
Epoch 1/2 -  Batch 55/936 -  Discriminator loss: 0.4014 -  Generator loss: 2.9526 - 
Epoch 1/2 -  Batch 56/936 -  Discriminator loss: 0.3877 -  Generator loss: 3.7029 - 
Epoch 1/2 -  Batch 57/936 -  Discriminator loss: 0.4108 -  Generator loss: 2.7321 - 
Epoch 1/2 -  Batch 58/936 -  Discriminator loss: 0.3985 -  Generator loss: 3.9481 - 
Epoch 1/2 -  Batch 59/936 -  Discriminator loss: 0.4248 -  Generator loss: 2.5943 - 
Epoch 1/2 -  Batch 60/936 -  Discriminator loss: 0.4416 -  Generator loss: 4.5127 - 
Epoch 1/2 -  Batch 61/936 -  Discriminator loss: 0.4118 -  Generator loss: 2.7487 - 
Epoch 1/2 -  Batch 62/936 -  Discriminator loss: 0.3880 -  Generator loss: 3.3131 - 
Epoch 1/2 -  Batch 63/936 -  Discriminator loss: 0.3901 -  Generator loss: 3.2467 - 
Epoch 1/2 -  Batch 64/936 -  Discriminator loss: 0.4008 -  Generator loss: 2.8782 - 
Epoch 1/2 -  Batch 65/936 -  Discriminator loss: 0.3913 -  Generator loss: 3.4603 - 
Epoch 1/2 -  Batch 66/936 -  Discriminator loss: 0.4062 -  Generator loss: 2.7921 - 
Epoch 1/2 -  Batch 67/936 -  Discriminator loss: 0.3914 -  Generator loss: 3.3627 - 
Epoch 1/2 -  Batch 68/936 -  Discriminator loss: 0.4162 -  Generator loss: 2.7147 - 
Epoch 1/2 -  Batch 69/936 -  Discriminator loss: 0.4166 -  Generator loss: 4.0372 - 
Epoch 1/2 -  Batch 70/936 -  Discriminator loss: 0.4465 -  Generator loss: 2.4228 - 
Epoch 1/2 -  Batch 71/936 -  Discriminator loss: 0.4445 -  Generator loss: 4.3200 - 
Epoch 1/2 -  Batch 72/936 -  Discriminator loss: 0.4169 -  Generator loss: 2.6646 - 
Epoch 1/2 -  Batch 73/936 -  Discriminator loss: 0.3930 -  Generator loss: 3.3043 - 
Epoch 1/2 -  Batch 74/936 -  Discriminator loss: 0.4026 -  Generator loss: 2.8365 - 
Epoch 1/2 -  Batch 75/936 -  Discriminator loss: 0.3950 -  Generator loss: 3.2592 - 
Epoch 1/2 -  Batch 76/936 -  Discriminator loss: 0.3991 -  Generator loss: 2.8610 - 
Epoch 1/2 -  Batch 77/936 -  Discriminator loss: 0.3998 -  Generator loss: 3.4147 - 
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Epoch 2/2 -  Batch 545/936 -  Discriminator loss: 0.3888 -  Generator loss: 2.9236 - 
Epoch 2/2 -  Batch 546/936 -  Discriminator loss: 0.4171 -  Generator loss: 4.3983 - 
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Epoch 2/2 -  Batch 552/936 -  Discriminator loss: 0.3771 -  Generator loss: 3.2629 - 
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Epoch 2/2 -  Batch 597/936 -  Discriminator loss: 0.3798 -  Generator loss: 3.3171 - 
Epoch 2/2 -  Batch 598/936 -  Discriminator loss: 0.3836 -  Generator loss: 3.0672 - 
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Epoch 2/2 -  Batch 621/936 -  Discriminator loss: 0.3766 -  Generator loss: 3.3956 - 
Epoch 2/2 -  Batch 622/936 -  Discriminator loss: 0.3852 -  Generator loss: 2.9775 - 
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Epoch 2/2 -  Batch 628/936 -  Discriminator loss: 0.3806 -  Generator loss: 3.1603 - 
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Epoch 2/2 -  Batch 643/936 -  Discriminator loss: 0.3764 -  Generator loss: 3.2655 - 
Epoch 2/2 -  Batch 644/936 -  Discriminator loss: 0.3851 -  Generator loss: 3.0008 - 
Epoch 2/2 -  Batch 645/936 -  Discriminator loss: 0.3816 -  Generator loss: 3.7128 - 
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Epoch 2/2 -  Batch 651/936 -  Discriminator loss: 0.3874 -  Generator loss: 3.5672 - 
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Epoch 2/2 -  Batch 653/936 -  Discriminator loss: 0.3852 -  Generator loss: 3.6809 - 
Epoch 2/2 -  Batch 654/936 -  Discriminator loss: 0.3911 -  Generator loss: 2.9197 - 
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Epoch 2/2 -  Batch 656/936 -  Discriminator loss: 0.3820 -  Generator loss: 3.1307 - 
Epoch 2/2 -  Batch 657/936 -  Discriminator loss: 0.3771 -  Generator loss: 3.2696 - 
Epoch 2/2 -  Batch 658/936 -  Discriminator loss: 0.3801 -  Generator loss: 3.2018 - 
Epoch 2/2 -  Batch 659/936 -  Discriminator loss: 0.3798 -  Generator loss: 3.1239 - 
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Epoch 2/2 -  Batch 661/936 -  Discriminator loss: 0.3842 -  Generator loss: 3.0383 - 
Epoch 2/2 -  Batch 662/936 -  Discriminator loss: 0.3816 -  Generator loss: 3.7058 - 
Epoch 2/2 -  Batch 663/936 -  Discriminator loss: 0.3966 -  Generator loss: 2.8346 - 
Epoch 2/2 -  Batch 664/936 -  Discriminator loss: 0.3925 -  Generator loss: 3.8196 - 
Epoch 2/2 -  Batch 665/936 -  Discriminator loss: 0.3971 -  Generator loss: 2.8340 - 
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Epoch 2/2 -  Batch 671/936 -  Discriminator loss: 0.3754 -  Generator loss: 3.2462 - 
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Epoch 2/2 -  Batch 687/936 -  Discriminator loss: 0.3800 -  Generator loss: 3.4240 - 
Epoch 2/2 -  Batch 688/936 -  Discriminator loss: 0.3875 -  Generator loss: 2.9513 - 
Epoch 2/2 -  Batch 689/936 -  Discriminator loss: 0.3800 -  Generator loss: 3.7693 - 
Epoch 2/2 -  Batch 690/936 -  Discriminator loss: 0.3971 -  Generator loss: 2.8687 - 
Epoch 2/2 -  Batch 691/936 -  Discriminator loss: 0.3898 -  Generator loss: 4.0300 - 
Epoch 2/2 -  Batch 692/936 -  Discriminator loss: 0.4061 -  Generator loss: 2.8048 - 
Epoch 2/2 -  Batch 693/936 -  Discriminator loss: 0.3959 -  Generator loss: 3.9363 - 
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Epoch 2/2 -  Batch 711/936 -  Discriminator loss: 0.3902 -  Generator loss: 2.9329 - 
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Epoch 2/2 -  Batch 716/936 -  Discriminator loss: 0.3802 -  Generator loss: 3.1007 - 
Epoch 2/2 -  Batch 717/936 -  Discriminator loss: 0.3776 -  Generator loss: 3.3090 - 
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Epoch 2/2 -  Batch 719/936 -  Discriminator loss: 0.3792 -  Generator loss: 3.2689 - 
Epoch 2/2 -  Batch 720/936 -  Discriminator loss: 0.3827 -  Generator loss: 3.0693 - 
Epoch 2/2 -  Batch 721/936 -  Discriminator loss: 0.3764 -  Generator loss: 3.4426 - 
Epoch 2/2 -  Batch 722/936 -  Discriminator loss: 0.3776 -  Generator loss: 3.2020 - 
Epoch 2/2 -  Batch 723/936 -  Discriminator loss: 0.3796 -  Generator loss: 3.1501 - 
Epoch 2/2 -  Batch 724/936 -  Discriminator loss: 0.3747 -  Generator loss: 3.2805 - 
Epoch 2/2 -  Batch 725/936 -  Discriminator loss: 0.3781 -  Generator loss: 3.2143 - 
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Epoch 2/2 -  Batch 728/936 -  Discriminator loss: 0.3754 -  Generator loss: 3.4467 - 
Epoch 2/2 -  Batch 729/936 -  Discriminator loss: 0.3818 -  Generator loss: 3.0605 - 
Epoch 2/2 -  Batch 730/936 -  Discriminator loss: 0.3789 -  Generator loss: 3.6994 - 
Epoch 2/2 -  Batch 731/936 -  Discriminator loss: 0.3917 -  Generator loss: 2.8997 - 
Epoch 2/2 -  Batch 732/936 -  Discriminator loss: 0.3785 -  Generator loss: 3.5453 - 
Epoch 2/2 -  Batch 733/936 -  Discriminator loss: 0.3792 -  Generator loss: 3.1697 - 
Epoch 2/2 -  Batch 734/936 -  Discriminator loss: 0.3765 -  Generator loss: 3.2368 - 
Epoch 2/2 -  Batch 735/936 -  Discriminator loss: 0.3793 -  Generator loss: 3.2008 - 
Epoch 2/2 -  Batch 736/936 -  Discriminator loss: 0.3810 -  Generator loss: 3.1757 - 
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Epoch 2/2 -  Batch 740/936 -  Discriminator loss: 0.3774 -  Generator loss: 3.6321 - 
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Epoch 2/2 -  Batch 742/936 -  Discriminator loss: 0.3978 -  Generator loss: 4.0459 - 
Epoch 2/2 -  Batch 743/936 -  Discriminator loss: 0.4056 -  Generator loss: 2.7476 - 
Epoch 2/2 -  Batch 744/936 -  Discriminator loss: 0.4016 -  Generator loss: 4.0286 - 
Epoch 2/2 -  Batch 745/936 -  Discriminator loss: 0.3986 -  Generator loss: 2.9039 - 
Epoch 2/2 -  Batch 746/936 -  Discriminator loss: 0.3844 -  Generator loss: 3.6334 - 
Epoch 2/2 -  Batch 747/936 -  Discriminator loss: 0.3844 -  Generator loss: 3.0435 - 
Epoch 2/2 -  Batch 748/936 -  Discriminator loss: 0.3802 -  Generator loss: 3.5423 - 
Epoch 2/2 -  Batch 749/936 -  Discriminator loss: 0.3825 -  Generator loss: 3.0496 - 
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Epoch 2/2 -  Batch 751/936 -  Discriminator loss: 0.3894 -  Generator loss: 2.9849 - 
Epoch 2/2 -  Batch 752/936 -  Discriminator loss: 0.3766 -  Generator loss: 3.6142 - 
Epoch 2/2 -  Batch 753/936 -  Discriminator loss: 0.3912 -  Generator loss: 2.9420 - 
Epoch 2/2 -  Batch 754/936 -  Discriminator loss: 0.3786 -  Generator loss: 3.5006 - 
Epoch 2/2 -  Batch 755/936 -  Discriminator loss: 0.3956 -  Generator loss: 2.8678 - 
Epoch 2/2 -  Batch 756/936 -  Discriminator loss: 0.3966 -  Generator loss: 3.8901 - 
Epoch 2/2 -  Batch 757/936 -  Discriminator loss: 0.4037 -  Generator loss: 2.7655 - 
Epoch 2/2 -  Batch 758/936 -  Discriminator loss: 0.4077 -  Generator loss: 4.2993 - 
Epoch 2/2 -  Batch 759/936 -  Discriminator loss: 0.4028 -  Generator loss: 2.8694 - 
Epoch 2/2 -  Batch 760/936 -  Discriminator loss: 0.3856 -  Generator loss: 3.5863 - 
Epoch 2/2 -  Batch 761/936 -  Discriminator loss: 0.3837 -  Generator loss: 3.0222 - 
Epoch 2/2 -  Batch 762/936 -  Discriminator loss: 0.3753 -  Generator loss: 3.4548 - 
Epoch 2/2 -  Batch 763/936 -  Discriminator loss: 0.3779 -  Generator loss: 3.2433 - 
Epoch 2/2 -  Batch 764/936 -  Discriminator loss: 0.3787 -  Generator loss: 3.1384 - 
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Epoch 2/2 -  Batch 766/936 -  Discriminator loss: 0.3889 -  Generator loss: 2.9054 - 
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Epoch 2/2 -  Batch 768/936 -  Discriminator loss: 0.3880 -  Generator loss: 2.9512 - 
Epoch 2/2 -  Batch 769/936 -  Discriminator loss: 0.3829 -  Generator loss: 3.7736 - 
Epoch 2/2 -  Batch 770/936 -  Discriminator loss: 0.3909 -  Generator loss: 2.9155 - 
Epoch 2/2 -  Batch 771/936 -  Discriminator loss: 0.3935 -  Generator loss: 4.0145 - 
Epoch 2/2 -  Batch 772/936 -  Discriminator loss: 0.3919 -  Generator loss: 2.9095 - 
Epoch 2/2 -  Batch 773/936 -  Discriminator loss: 0.3781 -  Generator loss: 3.5283 - 
Epoch 2/2 -  Batch 774/936 -  Discriminator loss: 0.3855 -  Generator loss: 3.0020 - 
Epoch 2/2 -  Batch 775/936 -  Discriminator loss: 0.3774 -  Generator loss: 3.4421 - 
Epoch 2/2 -  Batch 776/936 -  Discriminator loss: 0.3830 -  Generator loss: 3.0399 - 
Epoch 2/2 -  Batch 777/936 -  Discriminator loss: 0.3765 -  Generator loss: 3.3713 - 
Epoch 2/2 -  Batch 778/936 -  Discriminator loss: 0.3728 -  Generator loss: 3.2966 - 
Epoch 2/2 -  Batch 779/936 -  Discriminator loss: 0.3813 -  Generator loss: 3.1211 - 
Epoch 2/2 -  Batch 780/936 -  Discriminator loss: 0.3774 -  Generator loss: 3.3044 - 
Epoch 2/2 -  Batch 781/936 -  Discriminator loss: 0.3781 -  Generator loss: 3.2502 - 
Epoch 2/2 -  Batch 782/936 -  Discriminator loss: 0.3752 -  Generator loss: 3.2865 - 
Epoch 2/2 -  Batch 783/936 -  Discriminator loss: 0.3745 -  Generator loss: 3.2893 - 
Epoch 2/2 -  Batch 784/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.2280 - 
Epoch 2/2 -  Batch 785/936 -  Discriminator loss: 0.3762 -  Generator loss: 3.4066 - 
Epoch 2/2 -  Batch 786/936 -  Discriminator loss: 0.3788 -  Generator loss: 3.1627 - 
Epoch 2/2 -  Batch 787/936 -  Discriminator loss: 0.3773 -  Generator loss: 3.2640 - 
Epoch 2/2 -  Batch 788/936 -  Discriminator loss: 0.3786 -  Generator loss: 3.3503 - 
Epoch 2/2 -  Batch 789/936 -  Discriminator loss: 0.3817 -  Generator loss: 3.0542 - 
Epoch 2/2 -  Batch 790/936 -  Discriminator loss: 0.3882 -  Generator loss: 3.9617 - 
Epoch 2/2 -  Batch 791/936 -  Discriminator loss: 0.4059 -  Generator loss: 2.7437 - 
Epoch 2/2 -  Batch 792/936 -  Discriminator loss: 0.4250 -  Generator loss: 4.4524 - 
Epoch 2/2 -  Batch 793/936 -  Discriminator loss: 0.3946 -  Generator loss: 2.9525 - 
Epoch 2/2 -  Batch 794/936 -  Discriminator loss: 0.3754 -  Generator loss: 3.4717 - 
Epoch 2/2 -  Batch 795/936 -  Discriminator loss: 0.3879 -  Generator loss: 2.9638 - 
Epoch 2/2 -  Batch 796/936 -  Discriminator loss: 0.3824 -  Generator loss: 3.7545 - 
Epoch 2/2 -  Batch 797/936 -  Discriminator loss: 0.4011 -  Generator loss: 2.7868 - 
Epoch 2/2 -  Batch 798/936 -  Discriminator loss: 0.3966 -  Generator loss: 3.9023 - 
Epoch 2/2 -  Batch 799/936 -  Discriminator loss: 0.3950 -  Generator loss: 2.9148 - 
Epoch 2/2 -  Batch 800/936 -  Discriminator loss: 0.3829 -  Generator loss: 3.7431 - 
Epoch 2/2 -  Batch 801/936 -  Discriminator loss: 0.3892 -  Generator loss: 2.9765 - 
Epoch 2/2 -  Batch 802/936 -  Discriminator loss: 0.3788 -  Generator loss: 3.2982 - 
Epoch 2/2 -  Batch 803/936 -  Discriminator loss: 0.3782 -  Generator loss: 3.2343 - 
Epoch 2/2 -  Batch 804/936 -  Discriminator loss: 0.3759 -  Generator loss: 3.2855 - 
Epoch 2/2 -  Batch 805/936 -  Discriminator loss: 0.3780 -  Generator loss: 3.2188 - 
Epoch 2/2 -  Batch 806/936 -  Discriminator loss: 0.3785 -  Generator loss: 3.2871 - 
Epoch 2/2 -  Batch 807/936 -  Discriminator loss: 0.3769 -  Generator loss: 3.2275 - 
Epoch 2/2 -  Batch 808/936 -  Discriminator loss: 0.3830 -  Generator loss: 3.0751 - 
Epoch 2/2 -  Batch 809/936 -  Discriminator loss: 0.3807 -  Generator loss: 3.4484 - 
Epoch 2/2 -  Batch 810/936 -  Discriminator loss: 0.3848 -  Generator loss: 2.9877 - 
Epoch 2/2 -  Batch 811/936 -  Discriminator loss: 0.3877 -  Generator loss: 3.4112 - 
Epoch 2/2 -  Batch 812/936 -  Discriminator loss: 0.3857 -  Generator loss: 2.9975 - 
Epoch 2/2 -  Batch 813/936 -  Discriminator loss: 0.3830 -  Generator loss: 3.7681 - 
Epoch 2/2 -  Batch 814/936 -  Discriminator loss: 0.3980 -  Generator loss: 2.8292 - 
Epoch 2/2 -  Batch 815/936 -  Discriminator loss: 0.3986 -  Generator loss: 3.9944 - 
Epoch 2/2 -  Batch 816/936 -  Discriminator loss: 0.3967 -  Generator loss: 2.8551 - 
Epoch 2/2 -  Batch 817/936 -  Discriminator loss: 0.3837 -  Generator loss: 3.8416 - 
Epoch 2/2 -  Batch 818/936 -  Discriminator loss: 0.3870 -  Generator loss: 3.0060 - 
Epoch 2/2 -  Batch 819/936 -  Discriminator loss: 0.3867 -  Generator loss: 3.7274 - 
Epoch 2/2 -  Batch 820/936 -  Discriminator loss: 0.3883 -  Generator loss: 3.0132 - 
Epoch 2/2 -  Batch 821/936 -  Discriminator loss: 0.3808 -  Generator loss: 3.6826 - 
Epoch 2/2 -  Batch 822/936 -  Discriminator loss: 0.3870 -  Generator loss: 2.9716 - 
Epoch 2/2 -  Batch 823/936 -  Discriminator loss: 0.3865 -  Generator loss: 3.8468 - 
Epoch 2/2 -  Batch 824/936 -  Discriminator loss: 0.3998 -  Generator loss: 2.8559 - 
Epoch 2/2 -  Batch 825/936 -  Discriminator loss: 0.3907 -  Generator loss: 3.9428 - 
Epoch 2/2 -  Batch 826/936 -  Discriminator loss: 0.3903 -  Generator loss: 2.9870 - 
Epoch 2/2 -  Batch 827/936 -  Discriminator loss: 0.3810 -  Generator loss: 3.5302 - 
Epoch 2/2 -  Batch 828/936 -  Discriminator loss: 0.3861 -  Generator loss: 3.0082 - 
Epoch 2/2 -  Batch 829/936 -  Discriminator loss: 0.3801 -  Generator loss: 3.5517 - 
Epoch 2/2 -  Batch 830/936 -  Discriminator loss: 0.3812 -  Generator loss: 3.0845 - 
Epoch 2/2 -  Batch 831/936 -  Discriminator loss: 0.3786 -  Generator loss: 3.4755 - 
Epoch 2/2 -  Batch 832/936 -  Discriminator loss: 0.3827 -  Generator loss: 3.0674 - 
Epoch 2/2 -  Batch 833/936 -  Discriminator loss: 0.3784 -  Generator loss: 3.2342 - 
Epoch 2/2 -  Batch 834/936 -  Discriminator loss: 0.3745 -  Generator loss: 3.3027 - 
Epoch 2/2 -  Batch 835/936 -  Discriminator loss: 0.3767 -  Generator loss: 3.2532 - 
Epoch 2/2 -  Batch 836/936 -  Discriminator loss: 0.3778 -  Generator loss: 3.2439 - 
Epoch 2/2 -  Batch 837/936 -  Discriminator loss: 0.3798 -  Generator loss: 3.1845 - 
Epoch 2/2 -  Batch 838/936 -  Discriminator loss: 0.3794 -  Generator loss: 3.2318 - 
Epoch 2/2 -  Batch 839/936 -  Discriminator loss: 0.3773 -  Generator loss: 3.2310 - 
Epoch 2/2 -  Batch 840/936 -  Discriminator loss: 0.3747 -  Generator loss: 3.2648 - 
Epoch 2/2 -  Batch 841/936 -  Discriminator loss: 0.3755 -  Generator loss: 3.3373 - 
Epoch 2/2 -  Batch 842/936 -  Discriminator loss: 0.3798 -  Generator loss: 3.1478 - 
Epoch 2/2 -  Batch 843/936 -  Discriminator loss: 0.3719 -  Generator loss: 3.4371 - 
Epoch 2/2 -  Batch 844/936 -  Discriminator loss: 0.3765 -  Generator loss: 3.1776 - 
Epoch 2/2 -  Batch 845/936 -  Discriminator loss: 0.3748 -  Generator loss: 3.4047 - 
Epoch 2/2 -  Batch 846/936 -  Discriminator loss: 0.3850 -  Generator loss: 3.0622 - 
Epoch 2/2 -  Batch 847/936 -  Discriminator loss: 0.3857 -  Generator loss: 3.7935 - 
Epoch 2/2 -  Batch 848/936 -  Discriminator loss: 0.4166 -  Generator loss: 2.6538 - 
Epoch 2/2 -  Batch 849/936 -  Discriminator loss: 0.5033 -  Generator loss: 5.4657 - 
Epoch 2/2 -  Batch 850/936 -  Discriminator loss: 0.3817 -  Generator loss: 3.6657 - 
Epoch 2/2 -  Batch 851/936 -  Discriminator loss: 0.4738 -  Generator loss: 2.3375 - 
Epoch 2/2 -  Batch 852/936 -  Discriminator loss: 0.5744 -  Generator loss: 6.1882 - 
Epoch 2/2 -  Batch 853/936 -  Discriminator loss: 0.4573 -  Generator loss: 4.9371 - 
Epoch 2/2 -  Batch 854/936 -  Discriminator loss: 0.4327 -  Generator loss: 2.7436 - 
Epoch 2/2 -  Batch 855/936 -  Discriminator loss: 0.3878 -  Generator loss: 3.6482 - 
Epoch 2/2 -  Batch 856/936 -  Discriminator loss: 0.3948 -  Generator loss: 3.0978 - 
Epoch 2/2 -  Batch 857/936 -  Discriminator loss: 0.3802 -  Generator loss: 3.3325 - 
Epoch 2/2 -  Batch 858/936 -  Discriminator loss: 0.3821 -  Generator loss: 3.1000 - 
Epoch 2/2 -  Batch 859/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.3220 - 
Epoch 2/2 -  Batch 860/936 -  Discriminator loss: 0.3805 -  Generator loss: 3.3118 - 
Epoch 2/2 -  Batch 861/936 -  Discriminator loss: 0.3827 -  Generator loss: 3.0504 - 
Epoch 2/2 -  Batch 862/936 -  Discriminator loss: 0.3762 -  Generator loss: 3.3929 - 
Epoch 2/2 -  Batch 863/936 -  Discriminator loss: 0.3790 -  Generator loss: 3.2294 - 
Epoch 2/2 -  Batch 864/936 -  Discriminator loss: 0.3753 -  Generator loss: 3.2430 - 
Epoch 2/2 -  Batch 865/936 -  Discriminator loss: 0.3782 -  Generator loss: 3.2451 - 
Epoch 2/2 -  Batch 866/936 -  Discriminator loss: 0.3751 -  Generator loss: 3.3133 - 
Epoch 2/2 -  Batch 867/936 -  Discriminator loss: 0.3795 -  Generator loss: 3.1204 - 
Epoch 2/2 -  Batch 868/936 -  Discriminator loss: 0.3755 -  Generator loss: 3.3948 - 
Epoch 2/2 -  Batch 869/936 -  Discriminator loss: 0.3827 -  Generator loss: 3.0578 - 
Epoch 2/2 -  Batch 870/936 -  Discriminator loss: 0.3798 -  Generator loss: 3.4948 - 
Epoch 2/2 -  Batch 871/936 -  Discriminator loss: 0.3810 -  Generator loss: 3.1155 - 
Epoch 2/2 -  Batch 872/936 -  Discriminator loss: 0.3728 -  Generator loss: 3.4085 - 
Epoch 2/2 -  Batch 873/936 -  Discriminator loss: 0.3749 -  Generator loss: 3.2311 - 
Epoch 2/2 -  Batch 874/936 -  Discriminator loss: 0.3819 -  Generator loss: 3.0987 - 
Epoch 2/2 -  Batch 875/936 -  Discriminator loss: 0.3761 -  Generator loss: 3.5874 - 
Epoch 2/2 -  Batch 876/936 -  Discriminator loss: 0.3854 -  Generator loss: 2.9791 - 
Epoch 2/2 -  Batch 877/936 -  Discriminator loss: 0.3760 -  Generator loss: 3.3545 - 
Epoch 2/2 -  Batch 878/936 -  Discriminator loss: 0.3819 -  Generator loss: 3.1145 - 
Epoch 2/2 -  Batch 879/936 -  Discriminator loss: 0.3770 -  Generator loss: 3.2024 - 
Epoch 2/2 -  Batch 880/936 -  Discriminator loss: 0.3767 -  Generator loss: 3.3535 - 
Epoch 2/2 -  Batch 881/936 -  Discriminator loss: 0.3824 -  Generator loss: 3.1077 - 
Epoch 2/2 -  Batch 882/936 -  Discriminator loss: 0.3757 -  Generator loss: 3.3772 - 
Epoch 2/2 -  Batch 883/936 -  Discriminator loss: 0.3758 -  Generator loss: 3.2325 - 
Epoch 2/2 -  Batch 884/936 -  Discriminator loss: 0.3758 -  Generator loss: 3.2067 - 
Epoch 2/2 -  Batch 885/936 -  Discriminator loss: 0.3756 -  Generator loss: 3.3231 - 
Epoch 2/2 -  Batch 886/936 -  Discriminator loss: 0.3760 -  Generator loss: 3.2518 - 
Epoch 2/2 -  Batch 887/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.2261 - 
Epoch 2/2 -  Batch 888/936 -  Discriminator loss: 0.3848 -  Generator loss: 3.0717 - 
Epoch 2/2 -  Batch 889/936 -  Discriminator loss: 0.3765 -  Generator loss: 3.3936 - 
Epoch 2/2 -  Batch 890/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.1725 - 
Epoch 2/2 -  Batch 891/936 -  Discriminator loss: 0.3773 -  Generator loss: 3.3485 - 
Epoch 2/2 -  Batch 892/936 -  Discriminator loss: 0.3829 -  Generator loss: 3.0627 - 
Epoch 2/2 -  Batch 893/936 -  Discriminator loss: 0.3760 -  Generator loss: 3.6350 - 
Epoch 2/2 -  Batch 894/936 -  Discriminator loss: 0.3845 -  Generator loss: 3.0096 - 
Epoch 2/2 -  Batch 895/936 -  Discriminator loss: 0.3758 -  Generator loss: 3.5244 - 
Epoch 2/2 -  Batch 896/936 -  Discriminator loss: 0.3811 -  Generator loss: 3.0642 - 
Epoch 2/2 -  Batch 897/936 -  Discriminator loss: 0.3788 -  Generator loss: 3.3608 - 
Epoch 2/2 -  Batch 898/936 -  Discriminator loss: 0.3782 -  Generator loss: 3.2119 - 
Epoch 2/2 -  Batch 899/936 -  Discriminator loss: 0.3757 -  Generator loss: 3.2719 - 
Epoch 2/2 -  Batch 900/936 -  Discriminator loss: 0.3725 -  Generator loss: 3.2844 - 
Epoch 2/2 -  Batch 901/936 -  Discriminator loss: 0.3760 -  Generator loss: 3.3529 - 
Epoch 2/2 -  Batch 902/936 -  Discriminator loss: 0.3790 -  Generator loss: 3.1192 - 
Epoch 2/2 -  Batch 903/936 -  Discriminator loss: 0.3805 -  Generator loss: 3.4216 - 
Epoch 2/2 -  Batch 904/936 -  Discriminator loss: 0.3800 -  Generator loss: 3.1174 - 
Epoch 2/2 -  Batch 905/936 -  Discriminator loss: 0.3806 -  Generator loss: 3.1149 - 
Epoch 2/2 -  Batch 906/936 -  Discriminator loss: 0.3725 -  Generator loss: 3.6037 - 
Epoch 2/2 -  Batch 907/936 -  Discriminator loss: 0.3863 -  Generator loss: 2.9843 - 
Epoch 2/2 -  Batch 908/936 -  Discriminator loss: 0.3814 -  Generator loss: 3.7389 - 
Epoch 2/2 -  Batch 909/936 -  Discriminator loss: 0.3841 -  Generator loss: 3.0150 - 
Epoch 2/2 -  Batch 910/936 -  Discriminator loss: 0.3793 -  Generator loss: 3.4667 - 
Epoch 2/2 -  Batch 911/936 -  Discriminator loss: 0.3864 -  Generator loss: 2.9615 - 
Epoch 2/2 -  Batch 912/936 -  Discriminator loss: 0.3759 -  Generator loss: 3.6239 - 
Epoch 2/2 -  Batch 913/936 -  Discriminator loss: 0.3784 -  Generator loss: 3.0954 - 
Epoch 2/2 -  Batch 914/936 -  Discriminator loss: 0.3718 -  Generator loss: 3.5852 - 
Epoch 2/2 -  Batch 915/936 -  Discriminator loss: 0.3778 -  Generator loss: 3.1412 - 
Epoch 2/2 -  Batch 916/936 -  Discriminator loss: 0.3767 -  Generator loss: 3.3563 - 
Epoch 2/2 -  Batch 917/936 -  Discriminator loss: 0.3766 -  Generator loss: 3.2372 - 
Epoch 2/2 -  Batch 918/936 -  Discriminator loss: 0.3789 -  Generator loss: 3.1728 - 
Epoch 2/2 -  Batch 919/936 -  Discriminator loss: 0.3719 -  Generator loss: 3.4415 - 
Epoch 2/2 -  Batch 920/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.2378 - 
Epoch 2/2 -  Batch 921/936 -  Discriminator loss: 0.3848 -  Generator loss: 3.0123 - 
Epoch 2/2 -  Batch 922/936 -  Discriminator loss: 0.3769 -  Generator loss: 3.5346 - 
Epoch 2/2 -  Batch 923/936 -  Discriminator loss: 0.3850 -  Generator loss: 3.0021 - 
Epoch 2/2 -  Batch 924/936 -  Discriminator loss: 0.3779 -  Generator loss: 3.6796 - 
Epoch 2/2 -  Batch 925/936 -  Discriminator loss: 0.3918 -  Generator loss: 2.9009 - 
Epoch 2/2 -  Batch 926/936 -  Discriminator loss: 0.3971 -  Generator loss: 3.9166 - 
Epoch 2/2 -  Batch 927/936 -  Discriminator loss: 0.3933 -  Generator loss: 2.8895 - 
Epoch 2/2 -  Batch 928/936 -  Discriminator loss: 0.3772 -  Generator loss: 3.6993 - 
Epoch 2/2 -  Batch 929/936 -  Discriminator loss: 0.3914 -  Generator loss: 2.9152 - 
Epoch 2/2 -  Batch 930/936 -  Discriminator loss: 0.3864 -  Generator loss: 3.9739 - 
Epoch 2/2 -  Batch 931/936 -  Discriminator loss: 0.3957 -  Generator loss: 2.8538 - 
Epoch 2/2 -  Batch 932/936 -  Discriminator loss: 0.3909 -  Generator loss: 4.0235 - 
Epoch 2/2 -  Batch 933/936 -  Discriminator loss: 0.4024 -  Generator loss: 2.7631 - 
Epoch 2/2 -  Batch 934/936 -  Discriminator loss: 0.3861 -  Generator loss: 3.6149 - 
Epoch 2/2 -  Batch 935/936 -  Discriminator loss: 0.3846 -  Generator loss: 3.0378 - 
Epoch 2/2 -  Batch 936/936 -  Discriminator loss: 0.3766 -  Generator loss: 3.3438 - 
In [ ]:
 

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [181]:
batch_size = 128
z_dim = 128
learning_rate = 0.0001
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 -  Batch 1/1582 -  Discriminator loss: 1.3107 -  Generator loss: 0.5529 - 
Epoch 1/1 -  Batch 2/1582 -  Discriminator loss: 0.8683 -  Generator loss: 1.0690 - 
Epoch 1/1 -  Batch 3/1582 -  Discriminator loss: 0.7684 -  Generator loss: 1.3486 - 
Epoch 1/1 -  Batch 4/1582 -  Discriminator loss: 0.7182 -  Generator loss: 1.5084 - 
Epoch 1/1 -  Batch 5/1582 -  Discriminator loss: 0.6522 -  Generator loss: 1.6655 - 
Epoch 1/1 -  Batch 6/1582 -  Discriminator loss: 0.5907 -  Generator loss: 1.8739 - 
Epoch 1/1 -  Batch 7/1582 -  Discriminator loss: 0.5444 -  Generator loss: 1.9661 - 
Epoch 1/1 -  Batch 8/1582 -  Discriminator loss: 0.5292 -  Generator loss: 2.0743 - 
Epoch 1/1 -  Batch 9/1582 -  Discriminator loss: 0.5205 -  Generator loss: 2.1081 - 
Epoch 1/1 -  Batch 10/1582 -  Discriminator loss: 0.4997 -  Generator loss: 2.1716 - 
Epoch 1/1 -  Batch 11/1582 -  Discriminator loss: 0.5077 -  Generator loss: 2.1756 - 
Epoch 1/1 -  Batch 12/1582 -  Discriminator loss: 0.4789 -  Generator loss: 2.3697 - 
Epoch 1/1 -  Batch 13/1582 -  Discriminator loss: 0.4702 -  Generator loss: 2.3678 - 
Epoch 1/1 -  Batch 14/1582 -  Discriminator loss: 0.4597 -  Generator loss: 2.4626 - 
Epoch 1/1 -  Batch 15/1582 -  Discriminator loss: 0.4690 -  Generator loss: 2.4294 - 
Epoch 1/1 -  Batch 16/1582 -  Discriminator loss: 0.4561 -  Generator loss: 2.4587 - 
Epoch 1/1 -  Batch 17/1582 -  Discriminator loss: 0.4686 -  Generator loss: 2.4431 - 
Epoch 1/1 -  Batch 18/1582 -  Discriminator loss: 0.4630 -  Generator loss: 2.4346 - 
Epoch 1/1 -  Batch 19/1582 -  Discriminator loss: 0.4566 -  Generator loss: 2.5603 - 
Epoch 1/1 -  Batch 20/1582 -  Discriminator loss: 0.4654 -  Generator loss: 2.5315 - 
Epoch 1/1 -  Batch 21/1582 -  Discriminator loss: 0.4398 -  Generator loss: 2.6189 - 
Epoch 1/1 -  Batch 22/1582 -  Discriminator loss: 0.4351 -  Generator loss: 2.6345 - 
Epoch 1/1 -  Batch 23/1582 -  Discriminator loss: 0.4236 -  Generator loss: 2.7878 - 
Epoch 1/1 -  Batch 24/1582 -  Discriminator loss: 0.4278 -  Generator loss: 2.8036 - 
Epoch 1/1 -  Batch 25/1582 -  Discriminator loss: 0.4203 -  Generator loss: 2.8254 - 
Epoch 1/1 -  Batch 26/1582 -  Discriminator loss: 0.4151 -  Generator loss: 2.9110 - 
Epoch 1/1 -  Batch 27/1582 -  Discriminator loss: 0.4099 -  Generator loss: 3.0467 - 
Epoch 1/1 -  Batch 28/1582 -  Discriminator loss: 0.4086 -  Generator loss: 3.1461 - 
Epoch 1/1 -  Batch 29/1582 -  Discriminator loss: 0.3979 -  Generator loss: 3.0897 - 
Epoch 1/1 -  Batch 30/1582 -  Discriminator loss: 0.4048 -  Generator loss: 3.0687 - 
Epoch 1/1 -  Batch 31/1582 -  Discriminator loss: 0.4041 -  Generator loss: 3.0727 - 
Epoch 1/1 -  Batch 32/1582 -  Discriminator loss: 0.4049 -  Generator loss: 3.0838 - 
Epoch 1/1 -  Batch 33/1582 -  Discriminator loss: 0.3955 -  Generator loss: 3.1384 - 
Epoch 1/1 -  Batch 34/1582 -  Discriminator loss: 0.4036 -  Generator loss: 3.1035 - 
Epoch 1/1 -  Batch 35/1582 -  Discriminator loss: 0.3948 -  Generator loss: 3.1702 - 
Epoch 1/1 -  Batch 36/1582 -  Discriminator loss: 0.3933 -  Generator loss: 3.4458 - 
Epoch 1/1 -  Batch 37/1582 -  Discriminator loss: 0.3884 -  Generator loss: 3.3311 - 
Epoch 1/1 -  Batch 38/1582 -  Discriminator loss: 0.3895 -  Generator loss: 3.3134 - 
Epoch 1/1 -  Batch 39/1582 -  Discriminator loss: 0.3877 -  Generator loss: 3.5417 - 
Epoch 1/1 -  Batch 40/1582 -  Discriminator loss: 0.3866 -  Generator loss: 3.3969 - 
Epoch 1/1 -  Batch 41/1582 -  Discriminator loss: 0.3822 -  Generator loss: 3.5435 - 
Epoch 1/1 -  Batch 42/1582 -  Discriminator loss: 0.3907 -  Generator loss: 3.3540 - 
Epoch 1/1 -  Batch 43/1582 -  Discriminator loss: 0.3891 -  Generator loss: 3.2664 - 
Epoch 1/1 -  Batch 44/1582 -  Discriminator loss: 0.3952 -  Generator loss: 3.2199 - 
Epoch 1/1 -  Batch 45/1582 -  Discriminator loss: 0.3930 -  Generator loss: 3.2450 - 
Epoch 1/1 -  Batch 46/1582 -  Discriminator loss: 0.3835 -  Generator loss: 3.4149 - 
Epoch 1/1 -  Batch 47/1582 -  Discriminator loss: 0.3849 -  Generator loss: 3.3379 - 
Epoch 1/1 -  Batch 48/1582 -  Discriminator loss: 0.3863 -  Generator loss: 3.3220 - 
Epoch 1/1 -  Batch 49/1582 -  Discriminator loss: 0.3798 -  Generator loss: 3.4867 - 
Epoch 1/1 -  Batch 50/1582 -  Discriminator loss: 0.3709 -  Generator loss: 3.6170 - 
Epoch 1/1 -  Batch 51/1582 -  Discriminator loss: 0.3734 -  Generator loss: 3.7032 - 
Epoch 1/1 -  Batch 52/1582 -  Discriminator loss: 0.3704 -  Generator loss: 3.7015 - 
Epoch 1/1 -  Batch 53/1582 -  Discriminator loss: 0.3753 -  Generator loss: 3.5682 - 
Epoch 1/1 -  Batch 54/1582 -  Discriminator loss: 0.3708 -  Generator loss: 3.7530 - 
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Epoch 1/1 -  Batch 746/1582 -  Discriminator loss: 0.3642 -  Generator loss: 4.2047 - 
Epoch 1/1 -  Batch 747/1582 -  Discriminator loss: 0.3672 -  Generator loss: 3.4255 - 
Epoch 1/1 -  Batch 748/1582 -  Discriminator loss: 0.3674 -  Generator loss: 4.2449 - 
Epoch 1/1 -  Batch 749/1582 -  Discriminator loss: 0.3682 -  Generator loss: 3.4415 - 
Epoch 1/1 -  Batch 750/1582 -  Discriminator loss: 0.3652 -  Generator loss: 4.2743 - 
Epoch 1/1 -  Batch 751/1582 -  Discriminator loss: 0.3784 -  Generator loss: 3.1590 - 
Epoch 1/1 -  Batch 752/1582 -  Discriminator loss: 0.3864 -  Generator loss: 4.3967 - 
Epoch 1/1 -  Batch 753/1582 -  Discriminator loss: 0.3716 -  Generator loss: 3.3444 - 
Epoch 1/1 -  Batch 754/1582 -  Discriminator loss: 0.3663 -  Generator loss: 4.1383 - 
Epoch 1/1 -  Batch 755/1582 -  Discriminator loss: 0.3743 -  Generator loss: 3.2867 - 
Epoch 1/1 -  Batch 756/1582 -  Discriminator loss: 0.3752 -  Generator loss: 4.2856 - 
Epoch 1/1 -  Batch 757/1582 -  Discriminator loss: 0.3809 -  Generator loss: 3.2035 - 
Epoch 1/1 -  Batch 758/1582 -  Discriminator loss: 0.3787 -  Generator loss: 4.4281 - 
Epoch 1/1 -  Batch 759/1582 -  Discriminator loss: 0.3775 -  Generator loss: 3.1990 - 
Epoch 1/1 -  Batch 760/1582 -  Discriminator loss: 0.3712 -  Generator loss: 4.1997 - 
Epoch 1/1 -  Batch 761/1582 -  Discriminator loss: 0.3784 -  Generator loss: 3.2178 - 
Epoch 1/1 -  Batch 762/1582 -  Discriminator loss: 0.3739 -  Generator loss: 4.5013 - 
Epoch 1/1 -  Batch 763/1582 -  Discriminator loss: 0.3668 -  Generator loss: 3.4329 - 
Epoch 1/1 -  Batch 764/1582 -  Discriminator loss: 0.3606 -  Generator loss: 3.8667 - 
Epoch 1/1 -  Batch 765/1582 -  Discriminator loss: 0.3605 -  Generator loss: 3.6326 - 
Epoch 1/1 -  Batch 766/1582 -  Discriminator loss: 0.3590 -  Generator loss: 3.9154 - 
Epoch 1/1 -  Batch 767/1582 -  Discriminator loss: 0.3676 -  Generator loss: 3.3684 - 
Epoch 1/1 -  Batch 768/1582 -  Discriminator loss: 0.3777 -  Generator loss: 4.7192 - 
Epoch 1/1 -  Batch 769/1582 -  Discriminator loss: 0.3834 -  Generator loss: 3.0810 - 
Epoch 1/1 -  Batch 770/1582 -  Discriminator loss: 0.3903 -  Generator loss: 4.6477 - 
Epoch 1/1 -  Batch 771/1582 -  Discriminator loss: 0.3755 -  Generator loss: 3.2868 - 
Epoch 1/1 -  Batch 772/1582 -  Discriminator loss: 0.3681 -  Generator loss: 4.1688 - 
Epoch 1/1 -  Batch 773/1582 -  Discriminator loss: 0.3888 -  Generator loss: 3.0615 - 
Epoch 1/1 -  Batch 774/1582 -  Discriminator loss: 0.4429 -  Generator loss: 5.3800 - 
Epoch 1/1 -  Batch 775/1582 -  Discriminator loss: 0.3793 -  Generator loss: 3.3706 - 
Epoch 1/1 -  Batch 776/1582 -  Discriminator loss: 0.3952 -  Generator loss: 2.8791 - 
Epoch 1/1 -  Batch 777/1582 -  Discriminator loss: 0.3951 -  Generator loss: 4.2179 - 
Epoch 1/1 -  Batch 778/1582 -  Discriminator loss: 0.3680 -  Generator loss: 3.4420 - 
Epoch 1/1 -  Batch 779/1582 -  Discriminator loss: 0.3597 -  Generator loss: 3.8972 - 
Epoch 1/1 -  Batch 780/1582 -  Discriminator loss: 0.3640 -  Generator loss: 3.5056 - 
Epoch 1/1 -  Batch 781/1582 -  Discriminator loss: 0.3730 -  Generator loss: 4.0360 - 
Epoch 1/1 -  Batch 782/1582 -  Discriminator loss: 0.3683 -  Generator loss: 3.4748 - 
Epoch 1/1 -  Batch 783/1582 -  Discriminator loss: 0.3585 -  Generator loss: 3.9962 - 
Epoch 1/1 -  Batch 784/1582 -  Discriminator loss: 0.3612 -  Generator loss: 3.5797 - 
Epoch 1/1 -  Batch 785/1582 -  Discriminator loss: 0.3662 -  Generator loss: 3.9339 - 
Epoch 1/1 -  Batch 786/1582 -  Discriminator loss: 0.3636 -  Generator loss: 3.5593 - 
Epoch 1/1 -  Batch 787/1582 -  Discriminator loss: 0.3789 -  Generator loss: 4.5199 - 
Epoch 1/1 -  Batch 788/1582 -  Discriminator loss: 0.3629 -  Generator loss: 3.6006 - 
Epoch 1/1 -  Batch 789/1582 -  Discriminator loss: 0.3678 -  Generator loss: 3.4898 - 
Epoch 1/1 -  Batch 790/1582 -  Discriminator loss: 0.3685 -  Generator loss: 4.3673 - 
Epoch 1/1 -  Batch 791/1582 -  Discriminator loss: 0.3755 -  Generator loss: 3.2821 - 
Epoch 1/1 -  Batch 792/1582 -  Discriminator loss: 0.3640 -  Generator loss: 3.8830 - 
Epoch 1/1 -  Batch 793/1582 -  Discriminator loss: 0.3623 -  Generator loss: 3.6623 - 
Epoch 1/1 -  Batch 794/1582 -  Discriminator loss: 0.3548 -  Generator loss: 4.1643 - 
Epoch 1/1 -  Batch 795/1582 -  Discriminator loss: 0.3674 -  Generator loss: 3.4513 - 
Epoch 1/1 -  Batch 796/1582 -  Discriminator loss: 0.3727 -  Generator loss: 4.3643 - 
Epoch 1/1 -  Batch 797/1582 -  Discriminator loss: 0.3727 -  Generator loss: 3.3155 - 
Epoch 1/1 -  Batch 798/1582 -  Discriminator loss: 0.3671 -  Generator loss: 4.3887 - 
Epoch 1/1 -  Batch 799/1582 -  Discriminator loss: 0.3752 -  Generator loss: 3.2998 - 
Epoch 1/1 -  Batch 800/1582 -  Discriminator loss: 0.3843 -  Generator loss: 4.6003 - 
Epoch 1/1 -  Batch 801/1582 -  Discriminator loss: 0.3808 -  Generator loss: 3.1069 - 
Epoch 1/1 -  Batch 802/1582 -  Discriminator loss: 0.3833 -  Generator loss: 4.4238 - 
Epoch 1/1 -  Batch 803/1582 -  Discriminator loss: 0.3728 -  Generator loss: 3.2897 - 
Epoch 1/1 -  Batch 804/1582 -  Discriminator loss: 0.3637 -  Generator loss: 4.4210 - 
Epoch 1/1 -  Batch 805/1582 -  Discriminator loss: 0.3699 -  Generator loss: 3.3470 - 
Epoch 1/1 -  Batch 806/1582 -  Discriminator loss: 0.3613 -  Generator loss: 3.8455 - 
Epoch 1/1 -  Batch 807/1582 -  Discriminator loss: 0.3597 -  Generator loss: 3.6964 - 
Epoch 1/1 -  Batch 808/1582 -  Discriminator loss: 0.3560 -  Generator loss: 4.0081 - 
Epoch 1/1 -  Batch 809/1582 -  Discriminator loss: 0.3652 -  Generator loss: 3.4615 - 
Epoch 1/1 -  Batch 810/1582 -  Discriminator loss: 0.3632 -  Generator loss: 4.3671 - 
Epoch 1/1 -  Batch 811/1582 -  Discriminator loss: 0.3702 -  Generator loss: 3.4586 - 
Epoch 1/1 -  Batch 812/1582 -  Discriminator loss: 0.3859 -  Generator loss: 4.7199 - 
Epoch 1/1 -  Batch 813/1582 -  Discriminator loss: 0.3862 -  Generator loss: 3.0462 - 
Epoch 1/1 -  Batch 814/1582 -  Discriminator loss: 0.3974 -  Generator loss: 4.6049 - 
Epoch 1/1 -  Batch 815/1582 -  Discriminator loss: 0.3744 -  Generator loss: 3.2727 - 
Epoch 1/1 -  Batch 816/1582 -  Discriminator loss: 0.3659 -  Generator loss: 4.4122 - 
Epoch 1/1 -  Batch 817/1582 -  Discriminator loss: 0.3575 -  Generator loss: 3.7710 - 
Epoch 1/1 -  Batch 818/1582 -  Discriminator loss: 0.3622 -  Generator loss: 4.0561 - 
Epoch 1/1 -  Batch 819/1582 -  Discriminator loss: 0.3644 -  Generator loss: 3.5349 - 
Epoch 1/1 -  Batch 820/1582 -  Discriminator loss: 0.3703 -  Generator loss: 4.0938 - 
Epoch 1/1 -  Batch 821/1582 -  Discriminator loss: 0.3731 -  Generator loss: 3.2903 - 
Epoch 1/1 -  Batch 822/1582 -  Discriminator loss: 0.3749 -  Generator loss: 4.3951 - 
Epoch 1/1 -  Batch 823/1582 -  Discriminator loss: 0.3743 -  Generator loss: 3.3910 - 
Epoch 1/1 -  Batch 824/1582 -  Discriminator loss: 0.3919 -  Generator loss: 4.5343 - 
Epoch 1/1 -  Batch 825/1582 -  Discriminator loss: 0.3697 -  Generator loss: 3.4472 - 
Epoch 1/1 -  Batch 826/1582 -  Discriminator loss: 0.3660 -  Generator loss: 3.5384 - 
Epoch 1/1 -  Batch 827/1582 -  Discriminator loss: 0.3598 -  Generator loss: 4.2288 - 
Epoch 1/1 -  Batch 828/1582 -  Discriminator loss: 0.3658 -  Generator loss: 3.4845 - 
Epoch 1/1 -  Batch 829/1582 -  Discriminator loss: 0.3709 -  Generator loss: 4.2869 - 
Epoch 1/1 -  Batch 830/1582 -  Discriminator loss: 0.3733 -  Generator loss: 3.2844 - 
Epoch 1/1 -  Batch 831/1582 -  Discriminator loss: 0.3754 -  Generator loss: 4.4379 - 
Epoch 1/1 -  Batch 832/1582 -  Discriminator loss: 0.3640 -  Generator loss: 3.6007 - 
Epoch 1/1 -  Batch 833/1582 -  Discriminator loss: 0.3612 -  Generator loss: 3.8904 - 
Epoch 1/1 -  Batch 834/1582 -  Discriminator loss: 0.3675 -  Generator loss: 3.4792 - 
Epoch 1/1 -  Batch 835/1582 -  Discriminator loss: 0.3610 -  Generator loss: 4.0290 - 
Epoch 1/1 -  Batch 836/1582 -  Discriminator loss: 0.3690 -  Generator loss: 3.4013 - 
Epoch 1/1 -  Batch 837/1582 -  Discriminator loss: 0.3543 -  Generator loss: 4.2852 - 
Epoch 1/1 -  Batch 838/1582 -  Discriminator loss: 0.3590 -  Generator loss: 3.7381 - 
Epoch 1/1 -  Batch 839/1582 -  Discriminator loss: 0.3578 -  Generator loss: 3.9132 - 
Epoch 1/1 -  Batch 840/1582 -  Discriminator loss: 0.3701 -  Generator loss: 3.3032 - 
Epoch 1/1 -  Batch 841/1582 -  Discriminator loss: 0.3817 -  Generator loss: 4.8151 - 
Epoch 1/1 -  Batch 842/1582 -  Discriminator loss: 0.3726 -  Generator loss: 3.3503 - 
Epoch 1/1 -  Batch 843/1582 -  Discriminator loss: 0.3699 -  Generator loss: 4.7729 - 
Epoch 1/1 -  Batch 844/1582 -  Discriminator loss: 0.3750 -  Generator loss: 3.2867 - 
Epoch 1/1 -  Batch 845/1582 -  Discriminator loss: 0.3696 -  Generator loss: 3.8161 - 
Epoch 1/1 -  Batch 846/1582 -  Discriminator loss: 0.3748 -  Generator loss: 3.2787 - 
Epoch 1/1 -  Batch 847/1582 -  Discriminator loss: 0.3645 -  Generator loss: 4.2386 - 
Epoch 1/1 -  Batch 848/1582 -  Discriminator loss: 0.3638 -  Generator loss: 3.5387 - 
Epoch 1/1 -  Batch 849/1582 -  Discriminator loss: 0.3650 -  Generator loss: 4.5430 - 
Epoch 1/1 -  Batch 850/1582 -  Discriminator loss: 0.3864 -  Generator loss: 3.0741 - 
Epoch 1/1 -  Batch 851/1582 -  Discriminator loss: 0.4174 -  Generator loss: 4.8954 - 
Epoch 1/1 -  Batch 852/1582 -  Discriminator loss: 0.3743 -  Generator loss: 3.2873 - 
Epoch 1/1 -  Batch 853/1582 -  Discriminator loss: 0.3580 -  Generator loss: 3.9148 - 
Epoch 1/1 -  Batch 854/1582 -  Discriminator loss: 0.3691 -  Generator loss: 3.3682 - 
Epoch 1/1 -  Batch 855/1582 -  Discriminator loss: 0.3686 -  Generator loss: 3.9831 - 
Epoch 1/1 -  Batch 856/1582 -  Discriminator loss: 0.3653 -  Generator loss: 3.5151 - 
Epoch 1/1 -  Batch 857/1582 -  Discriminator loss: 0.3610 -  Generator loss: 4.5389 - 
Epoch 1/1 -  Batch 858/1582 -  Discriminator loss: 0.3677 -  Generator loss: 3.4205 - 
Epoch 1/1 -  Batch 859/1582 -  Discriminator loss: 0.3679 -  Generator loss: 3.9154 - 
Epoch 1/1 -  Batch 860/1582 -  Discriminator loss: 0.3622 -  Generator loss: 3.6030 - 
Epoch 1/1 -  Batch 861/1582 -  Discriminator loss: 0.3551 -  Generator loss: 4.1408 - 
Epoch 1/1 -  Batch 862/1582 -  Discriminator loss: 0.3636 -  Generator loss: 3.5239 - 
Epoch 1/1 -  Batch 863/1582 -  Discriminator loss: 0.3632 -  Generator loss: 4.0028 - 
Epoch 1/1 -  Batch 864/1582 -  Discriminator loss: 0.3622 -  Generator loss: 3.5348 - 
Epoch 1/1 -  Batch 865/1582 -  Discriminator loss: 0.3649 -  Generator loss: 4.3395 - 
Epoch 1/1 -  Batch 866/1582 -  Discriminator loss: 0.3675 -  Generator loss: 3.5637 - 
Epoch 1/1 -  Batch 867/1582 -  Discriminator loss: 0.3608 -  Generator loss: 4.0886 - 
Epoch 1/1 -  Batch 868/1582 -  Discriminator loss: 0.3752 -  Generator loss: 3.2063 - 
Epoch 1/1 -  Batch 869/1582 -  Discriminator loss: 0.3933 -  Generator loss: 4.5874 - 
Epoch 1/1 -  Batch 870/1582 -  Discriminator loss: 0.3986 -  Generator loss: 2.9361 - 
Epoch 1/1 -  Batch 871/1582 -  Discriminator loss: 0.4766 -  Generator loss: 5.4791 - 
Epoch 1/1 -  Batch 872/1582 -  Discriminator loss: 0.3813 -  Generator loss: 3.6699 - 
Epoch 1/1 -  Batch 873/1582 -  Discriminator loss: 0.4720 -  Generator loss: 2.2497 - 
Epoch 1/1 -  Batch 874/1582 -  Discriminator loss: 0.6345 -  Generator loss: 6.7955 - 
Epoch 1/1 -  Batch 875/1582 -  Discriminator loss: 0.5396 -  Generator loss: 5.6704 - 
Epoch 1/1 -  Batch 876/1582 -  Discriminator loss: 0.3949 -  Generator loss: 3.3562 - 
Epoch 1/1 -  Batch 877/1582 -  Discriminator loss: 0.4621 -  Generator loss: 2.3398 - 
Epoch 1/1 -  Batch 878/1582 -  Discriminator loss: 0.4782 -  Generator loss: 4.8424 - 
Epoch 1/1 -  Batch 879/1582 -  Discriminator loss: 0.4167 -  Generator loss: 4.3364 - 
Epoch 1/1 -  Batch 880/1582 -  Discriminator loss: 0.4049 -  Generator loss: 2.7381 - 
Epoch 1/1 -  Batch 881/1582 -  Discriminator loss: 0.3860 -  Generator loss: 3.2795 - 
Epoch 1/1 -  Batch 882/1582 -  Discriminator loss: 0.3861 -  Generator loss: 3.4147 - 
Epoch 1/1 -  Batch 883/1582 -  Discriminator loss: 0.3870 -  Generator loss: 3.1328 - 
Epoch 1/1 -  Batch 884/1582 -  Discriminator loss: 0.3842 -  Generator loss: 3.2266 - 
Epoch 1/1 -  Batch 885/1582 -  Discriminator loss: 0.3786 -  Generator loss: 3.4492 - 
Epoch 1/1 -  Batch 886/1582 -  Discriminator loss: 0.3743 -  Generator loss: 3.4220 - 
Epoch 1/1 -  Batch 887/1582 -  Discriminator loss: 0.3695 -  Generator loss: 3.5256 - 
Epoch 1/1 -  Batch 888/1582 -  Discriminator loss: 0.3686 -  Generator loss: 3.6233 - 
Epoch 1/1 -  Batch 889/1582 -  Discriminator loss: 0.3706 -  Generator loss: 3.4289 - 
Epoch 1/1 -  Batch 890/1582 -  Discriminator loss: 0.3686 -  Generator loss: 3.6234 - 
Epoch 1/1 -  Batch 891/1582 -  Discriminator loss: 0.3742 -  Generator loss: 3.3082 - 
Epoch 1/1 -  Batch 892/1582 -  Discriminator loss: 0.3695 -  Generator loss: 3.5854 - 
Epoch 1/1 -  Batch 893/1582 -  Discriminator loss: 0.3725 -  Generator loss: 3.4055 - 
Epoch 1/1 -  Batch 894/1582 -  Discriminator loss: 0.3688 -  Generator loss: 3.6748 - 
Epoch 1/1 -  Batch 895/1582 -  Discriminator loss: 0.3742 -  Generator loss: 3.2928 - 
Epoch 1/1 -  Batch 896/1582 -  Discriminator loss: 0.3720 -  Generator loss: 3.4624 - 
Epoch 1/1 -  Batch 897/1582 -  Discriminator loss: 0.3769 -  Generator loss: 3.3949 - 
Epoch 1/1 -  Batch 898/1582 -  Discriminator loss: 0.3712 -  Generator loss: 3.5738 - 
Epoch 1/1 -  Batch 899/1582 -  Discriminator loss: 0.3693 -  Generator loss: 3.5052 - 
Epoch 1/1 -  Batch 900/1582 -  Discriminator loss: 0.3634 -  Generator loss: 3.5895 - 
Epoch 1/1 -  Batch 901/1582 -  Discriminator loss: 0.3703 -  Generator loss: 3.4305 - 
Epoch 1/1 -  Batch 902/1582 -  Discriminator loss: 0.3665 -  Generator loss: 3.6301 - 
Epoch 1/1 -  Batch 903/1582 -  Discriminator loss: 0.3747 -  Generator loss: 3.3294 - 
Epoch 1/1 -  Batch 904/1582 -  Discriminator loss: 0.3682 -  Generator loss: 3.7655 - 
Epoch 1/1 -  Batch 905/1582 -  Discriminator loss: 0.3717 -  Generator loss: 3.3016 - 
Epoch 1/1 -  Batch 906/1582 -  Discriminator loss: 0.3657 -  Generator loss: 3.6246 - 
Epoch 1/1 -  Batch 907/1582 -  Discriminator loss: 0.3672 -  Generator loss: 3.6412 - 
Epoch 1/1 -  Batch 908/1582 -  Discriminator loss: 0.3697 -  Generator loss: 3.4234 - 
Epoch 1/1 -  Batch 909/1582 -  Discriminator loss: 0.3707 -  Generator loss: 3.4546 - 
Epoch 1/1 -  Batch 910/1582 -  Discriminator loss: 0.3639 -  Generator loss: 3.7654 - 
Epoch 1/1 -  Batch 911/1582 -  Discriminator loss: 0.3687 -  Generator loss: 3.3942 - 
Epoch 1/1 -  Batch 912/1582 -  Discriminator loss: 0.3638 -  Generator loss: 4.0071 - 
Epoch 1/1 -  Batch 913/1582 -  Discriminator loss: 0.3595 -  Generator loss: 3.6057 - 
Epoch 1/1 -  Batch 914/1582 -  Discriminator loss: 0.3552 -  Generator loss: 4.0237 - 
Epoch 1/1 -  Batch 915/1582 -  Discriminator loss: 0.3687 -  Generator loss: 3.3879 - 
Epoch 1/1 -  Batch 916/1582 -  Discriminator loss: 0.3686 -  Generator loss: 3.6467 - 
Epoch 1/1 -  Batch 917/1582 -  Discriminator loss: 0.3643 -  Generator loss: 3.5590 - 
Epoch 1/1 -  Batch 918/1582 -  Discriminator loss: 0.3599 -  Generator loss: 3.9967 - 
Epoch 1/1 -  Batch 919/1582 -  Discriminator loss: 0.3669 -  Generator loss: 3.4759 - 
Epoch 1/1 -  Batch 920/1582 -  Discriminator loss: 0.3601 -  Generator loss: 4.0172 - 
Epoch 1/1 -  Batch 921/1582 -  Discriminator loss: 0.3642 -  Generator loss: 3.5346 - 
Epoch 1/1 -  Batch 922/1582 -  Discriminator loss: 0.3589 -  Generator loss: 3.9196 - 
Epoch 1/1 -  Batch 923/1582 -  Discriminator loss: 0.3642 -  Generator loss: 3.5445 - 
Epoch 1/1 -  Batch 924/1582 -  Discriminator loss: 0.3625 -  Generator loss: 3.6938 - 
Epoch 1/1 -  Batch 925/1582 -  Discriminator loss: 0.3617 -  Generator loss: 3.6823 - 
Epoch 1/1 -  Batch 926/1582 -  Discriminator loss: 0.3602 -  Generator loss: 3.8198 - 
Epoch 1/1 -  Batch 927/1582 -  Discriminator loss: 0.3675 -  Generator loss: 3.3916 - 
Epoch 1/1 -  Batch 928/1582 -  Discriminator loss: 0.3705 -  Generator loss: 4.4600 - 
Epoch 1/1 -  Batch 929/1582 -  Discriminator loss: 0.3781 -  Generator loss: 3.1419 - 
Epoch 1/1 -  Batch 930/1582 -  Discriminator loss: 0.3789 -  Generator loss: 4.3983 - 
Epoch 1/1 -  Batch 931/1582 -  Discriminator loss: 0.3772 -  Generator loss: 3.1619 - 
Epoch 1/1 -  Batch 932/1582 -  Discriminator loss: 0.3658 -  Generator loss: 3.8125 - 
Epoch 1/1 -  Batch 933/1582 -  Discriminator loss: 0.3602 -  Generator loss: 3.8381 - 
Epoch 1/1 -  Batch 934/1582 -  Discriminator loss: 0.3548 -  Generator loss: 3.8039 - 
Epoch 1/1 -  Batch 935/1582 -  Discriminator loss: 0.3557 -  Generator loss: 4.0863 - 
Epoch 1/1 -  Batch 936/1582 -  Discriminator loss: 0.3604 -  Generator loss: 3.6727 - 
Epoch 1/1 -  Batch 937/1582 -  Discriminator loss: 0.3569 -  Generator loss: 3.8553 - 
Epoch 1/1 -  Batch 938/1582 -  Discriminator loss: 0.3558 -  Generator loss: 4.0484 - 
Epoch 1/1 -  Batch 939/1582 -  Discriminator loss: 0.3527 -  Generator loss: 3.9163 - 
Epoch 1/1 -  Batch 940/1582 -  Discriminator loss: 0.3554 -  Generator loss: 3.9052 - 
Epoch 1/1 -  Batch 941/1582 -  Discriminator loss: 0.3585 -  Generator loss: 3.7974 - 
Epoch 1/1 -  Batch 942/1582 -  Discriminator loss: 0.3658 -  Generator loss: 3.5027 - 
Epoch 1/1 -  Batch 943/1582 -  Discriminator loss: 0.3639 -  Generator loss: 4.4181 - 
Epoch 1/1 -  Batch 944/1582 -  Discriminator loss: 0.3678 -  Generator loss: 3.5009 - 
Epoch 1/1 -  Batch 945/1582 -  Discriminator loss: 0.3710 -  Generator loss: 3.9775 - 
Epoch 1/1 -  Batch 946/1582 -  Discriminator loss: 0.3621 -  Generator loss: 3.6710 - 
Epoch 1/1 -  Batch 947/1582 -  Discriminator loss: 0.3529 -  Generator loss: 3.9788 - 
Epoch 1/1 -  Batch 948/1582 -  Discriminator loss: 0.3571 -  Generator loss: 3.8683 - 
Epoch 1/1 -  Batch 949/1582 -  Discriminator loss: 0.3632 -  Generator loss: 3.5108 - 
Epoch 1/1 -  Batch 950/1582 -  Discriminator loss: 0.3624 -  Generator loss: 4.2314 - 
Epoch 1/1 -  Batch 951/1582 -  Discriminator loss: 0.3599 -  Generator loss: 3.6094 - 
Epoch 1/1 -  Batch 952/1582 -  Discriminator loss: 0.3775 -  Generator loss: 4.4344 - 
Epoch 1/1 -  Batch 953/1582 -  Discriminator loss: 0.3763 -  Generator loss: 3.2560 - 
Epoch 1/1 -  Batch 954/1582 -  Discriminator loss: 0.3716 -  Generator loss: 4.3218 - 
Epoch 1/1 -  Batch 955/1582 -  Discriminator loss: 0.3654 -  Generator loss: 3.4720 - 
Epoch 1/1 -  Batch 956/1582 -  Discriminator loss: 0.3601 -  Generator loss: 3.9080 - 
Epoch 1/1 -  Batch 957/1582 -  Discriminator loss: 0.3548 -  Generator loss: 4.0558 - 
Epoch 1/1 -  Batch 958/1582 -  Discriminator loss: 0.3602 -  Generator loss: 3.6020 - 
Epoch 1/1 -  Batch 959/1582 -  Discriminator loss: 0.3757 -  Generator loss: 4.5450 - 
Epoch 1/1 -  Batch 960/1582 -  Discriminator loss: 0.3712 -  Generator loss: 3.3468 - 
Epoch 1/1 -  Batch 961/1582 -  Discriminator loss: 0.3691 -  Generator loss: 4.5186 - 
Epoch 1/1 -  Batch 962/1582 -  Discriminator loss: 0.3700 -  Generator loss: 3.3934 - 
Epoch 1/1 -  Batch 963/1582 -  Discriminator loss: 0.3667 -  Generator loss: 4.1824 - 
Epoch 1/1 -  Batch 964/1582 -  Discriminator loss: 0.3652 -  Generator loss: 3.4438 - 
Epoch 1/1 -  Batch 965/1582 -  Discriminator loss: 0.3876 -  Generator loss: 4.8106 - 
Epoch 1/1 -  Batch 966/1582 -  Discriminator loss: 0.3705 -  Generator loss: 3.3923 - 
Epoch 1/1 -  Batch 967/1582 -  Discriminator loss: 0.3712 -  Generator loss: 4.1241 - 
Epoch 1/1 -  Batch 968/1582 -  Discriminator loss: 0.3718 -  Generator loss: 3.3240 - 
Epoch 1/1 -  Batch 969/1582 -  Discriminator loss: 0.3730 -  Generator loss: 4.2065 - 
Epoch 1/1 -  Batch 970/1582 -  Discriminator loss: 0.3653 -  Generator loss: 3.4663 - 
Epoch 1/1 -  Batch 971/1582 -  Discriminator loss: 0.3578 -  Generator loss: 3.8843 - 
Epoch 1/1 -  Batch 972/1582 -  Discriminator loss: 0.3524 -  Generator loss: 4.0273 - 
Epoch 1/1 -  Batch 973/1582 -  Discriminator loss: 0.3620 -  Generator loss: 3.6452 - 
Epoch 1/1 -  Batch 974/1582 -  Discriminator loss: 0.3567 -  Generator loss: 4.0316 - 
Epoch 1/1 -  Batch 975/1582 -  Discriminator loss: 0.3518 -  Generator loss: 4.0676 - 
Epoch 1/1 -  Batch 976/1582 -  Discriminator loss: 0.3590 -  Generator loss: 3.7655 - 
Epoch 1/1 -  Batch 977/1582 -  Discriminator loss: 0.3556 -  Generator loss: 4.1594 - 
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Epoch 1/1 -  Batch 980/1582 -  Discriminator loss: 0.3619 -  Generator loss: 3.5566 - 
Epoch 1/1 -  Batch 981/1582 -  Discriminator loss: 0.3703 -  Generator loss: 4.3831 - 
Epoch 1/1 -  Batch 982/1582 -  Discriminator loss: 0.3766 -  Generator loss: 3.3648 - 
Epoch 1/1 -  Batch 983/1582 -  Discriminator loss: 0.4040 -  Generator loss: 5.0170 - 
Epoch 1/1 -  Batch 984/1582 -  Discriminator loss: 0.3751 -  Generator loss: 3.3060 - 
Epoch 1/1 -  Batch 985/1582 -  Discriminator loss: 0.3618 -  Generator loss: 3.9192 - 
Epoch 1/1 -  Batch 986/1582 -  Discriminator loss: 0.3647 -  Generator loss: 3.5357 - 
Epoch 1/1 -  Batch 987/1582 -  Discriminator loss: 0.3588 -  Generator loss: 3.9775 - 
Epoch 1/1 -  Batch 988/1582 -  Discriminator loss: 0.3602 -  Generator loss: 3.6335 - 
Epoch 1/1 -  Batch 989/1582 -  Discriminator loss: 0.3586 -  Generator loss: 4.1911 - 
Epoch 1/1 -  Batch 990/1582 -  Discriminator loss: 0.3622 -  Generator loss: 3.5436 - 
Epoch 1/1 -  Batch 991/1582 -  Discriminator loss: 0.3660 -  Generator loss: 4.3939 - 
Epoch 1/1 -  Batch 992/1582 -  Discriminator loss: 0.3749 -  Generator loss: 3.3096 - 
Epoch 1/1 -  Batch 993/1582 -  Discriminator loss: 0.3842 -  Generator loss: 4.4618 - 
Epoch 1/1 -  Batch 994/1582 -  Discriminator loss: 0.3761 -  Generator loss: 3.2366 - 
Epoch 1/1 -  Batch 995/1582 -  Discriminator loss: 0.3809 -  Generator loss: 4.5785 - 
Epoch 1/1 -  Batch 996/1582 -  Discriminator loss: 0.3710 -  Generator loss: 3.3445 - 
Epoch 1/1 -  Batch 997/1582 -  Discriminator loss: 0.3682 -  Generator loss: 4.2289 - 
Epoch 1/1 -  Batch 998/1582 -  Discriminator loss: 0.3689 -  Generator loss: 3.3666 - 
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Epoch 1/1 -  Batch 1003/1582 -  Discriminator loss: 0.3652 -  Generator loss: 4.2431 - 
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Epoch 1/1 -  Batch 1005/1582 -  Discriminator loss: 0.3588 -  Generator loss: 4.2722 - 
Epoch 1/1 -  Batch 1006/1582 -  Discriminator loss: 0.3694 -  Generator loss: 3.3891 - 
Epoch 1/1 -  Batch 1007/1582 -  Discriminator loss: 0.3830 -  Generator loss: 4.4520 - 
Epoch 1/1 -  Batch 1008/1582 -  Discriminator loss: 0.3681 -  Generator loss: 3.4017 - 
Epoch 1/1 -  Batch 1009/1582 -  Discriminator loss: 0.3608 -  Generator loss: 4.3756 - 
Epoch 1/1 -  Batch 1010/1582 -  Discriminator loss: 0.3590 -  Generator loss: 3.6330 - 
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Epoch 1/1 -  Batch 1018/1582 -  Discriminator loss: 0.3608 -  Generator loss: 3.6298 - 
Epoch 1/1 -  Batch 1019/1582 -  Discriminator loss: 0.3552 -  Generator loss: 4.3019 - 
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Epoch 1/1 -  Batch 1022/1582 -  Discriminator loss: 0.3714 -  Generator loss: 3.3352 - 
Epoch 1/1 -  Batch 1023/1582 -  Discriminator loss: 0.3821 -  Generator loss: 4.4202 - 
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Epoch 1/1 -  Batch 1028/1582 -  Discriminator loss: 0.3647 -  Generator loss: 3.4805 - 
Epoch 1/1 -  Batch 1029/1582 -  Discriminator loss: 0.3640 -  Generator loss: 4.1179 - 
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Epoch 1/1 -  Batch 1035/1582 -  Discriminator loss: 0.3548 -  Generator loss: 3.8719 - 
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Epoch 1/1 -  Batch 1056/1582 -  Discriminator loss: 0.3593 -  Generator loss: 3.6262 - 
Epoch 1/1 -  Batch 1057/1582 -  Discriminator loss: 0.3602 -  Generator loss: 3.8595 - 
Epoch 1/1 -  Batch 1058/1582 -  Discriminator loss: 0.3536 -  Generator loss: 3.9368 - 
Epoch 1/1 -  Batch 1059/1582 -  Discriminator loss: 0.3451 -  Generator loss: 4.2487 - 
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Epoch 1/1 -  Batch 1072/1582 -  Discriminator loss: 0.3621 -  Generator loss: 3.6064 - 
Epoch 1/1 -  Batch 1073/1582 -  Discriminator loss: 0.3580 -  Generator loss: 3.7807 - 
Epoch 1/1 -  Batch 1074/1582 -  Discriminator loss: 0.3585 -  Generator loss: 3.8776 - 
Epoch 1/1 -  Batch 1075/1582 -  Discriminator loss: 0.3521 -  Generator loss: 3.9810 - 
Epoch 1/1 -  Batch 1076/1582 -  Discriminator loss: 0.3599 -  Generator loss: 3.7039 - 
Epoch 1/1 -  Batch 1077/1582 -  Discriminator loss: 0.3639 -  Generator loss: 3.6914 - 
Epoch 1/1 -  Batch 1078/1582 -  Discriminator loss: 0.3590 -  Generator loss: 3.6961 - 
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Epoch 1/1 -  Batch 1083/1582 -  Discriminator loss: 0.3555 -  Generator loss: 4.4567 - 
Epoch 1/1 -  Batch 1084/1582 -  Discriminator loss: 0.3645 -  Generator loss: 3.6565 - 
Epoch 1/1 -  Batch 1085/1582 -  Discriminator loss: 0.3984 -  Generator loss: 4.8598 - 
Epoch 1/1 -  Batch 1086/1582 -  Discriminator loss: 0.3808 -  Generator loss: 3.2316 - 
Epoch 1/1 -  Batch 1087/1582 -  Discriminator loss: 0.3780 -  Generator loss: 4.2411 - 
Epoch 1/1 -  Batch 1088/1582 -  Discriminator loss: 0.3783 -  Generator loss: 3.1899 - 
Epoch 1/1 -  Batch 1089/1582 -  Discriminator loss: 0.3849 -  Generator loss: 4.4699 - 
Epoch 1/1 -  Batch 1090/1582 -  Discriminator loss: 0.3791 -  Generator loss: 3.1378 - 
Epoch 1/1 -  Batch 1091/1582 -  Discriminator loss: 0.3945 -  Generator loss: 4.6921 - 
Epoch 1/1 -  Batch 1092/1582 -  Discriminator loss: 0.3712 -  Generator loss: 3.3246 - 
Epoch 1/1 -  Batch 1093/1582 -  Discriminator loss: 0.3619 -  Generator loss: 3.8208 - 
Epoch 1/1 -  Batch 1094/1582 -  Discriminator loss: 0.3642 -  Generator loss: 3.6706 - 
Epoch 1/1 -  Batch 1095/1582 -  Discriminator loss: 0.3595 -  Generator loss: 3.6252 - 
Epoch 1/1 -  Batch 1096/1582 -  Discriminator loss: 0.3593 -  Generator loss: 4.1629 - 
Epoch 1/1 -  Batch 1097/1582 -  Discriminator loss: 0.3530 -  Generator loss: 3.9175 - 
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Epoch 1/1 -  Batch 1136/1582 -  Discriminator loss: 0.3664 -  Generator loss: 4.0213 - 
Epoch 1/1 -  Batch 1137/1582 -  Discriminator loss: 0.3635 -  Generator loss: 3.5634 - 
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Epoch 1/1 -  Batch 1183/1582 -  Discriminator loss: 0.3628 -  Generator loss: 4.3274 - 
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Epoch 1/1 -  Batch 1190/1582 -  Discriminator loss: 0.3681 -  Generator loss: 4.1091 - 
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Epoch 1/1 -  Batch 1205/1582 -  Discriminator loss: 0.3633 -  Generator loss: 4.1612 - 
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Epoch 1/1 -  Batch 1207/1582 -  Discriminator loss: 0.3721 -  Generator loss: 4.5614 - 
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Epoch 1/1 -  Batch 1211/1582 -  Discriminator loss: 0.3555 -  Generator loss: 3.7928 - 
Epoch 1/1 -  Batch 1212/1582 -  Discriminator loss: 0.3655 -  Generator loss: 3.4365 - 
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Epoch 1/1 -  Batch 1216/1582 -  Discriminator loss: 0.3828 -  Generator loss: 3.0705 - 
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Epoch 1/1 -  Batch 1222/1582 -  Discriminator loss: 0.3621 -  Generator loss: 3.5821 - 
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Epoch 1/1 -  Batch 1298/1582 -  Discriminator loss: 0.3408 -  Generator loss: 4.6898 - 
Epoch 1/1 -  Batch 1299/1582 -  Discriminator loss: 0.3664 -  Generator loss: 3.3713 - 
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Epoch 1/1 -  Batch 1301/1582 -  Discriminator loss: 0.3728 -  Generator loss: 3.4986 - 
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Epoch 1/1 -  Batch 1303/1582 -  Discriminator loss: 0.3703 -  Generator loss: 3.3301 - 
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Epoch 1/1 -  Batch 1305/1582 -  Discriminator loss: 0.3888 -  Generator loss: 3.0595 - 
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Epoch 1/1 -  Batch 1307/1582 -  Discriminator loss: 0.3700 -  Generator loss: 3.4114 - 
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Epoch 1/1 -  Batch 1309/1582 -  Discriminator loss: 0.3650 -  Generator loss: 3.6369 - 
Epoch 1/1 -  Batch 1310/1582 -  Discriminator loss: 0.3540 -  Generator loss: 4.3452 - 
Epoch 1/1 -  Batch 1311/1582 -  Discriminator loss: 0.3756 -  Generator loss: 3.2023 - 
Epoch 1/1 -  Batch 1312/1582 -  Discriminator loss: 0.3951 -  Generator loss: 5.0927 - 
Epoch 1/1 -  Batch 1313/1582 -  Discriminator loss: 0.3861 -  Generator loss: 3.0684 - 
Epoch 1/1 -  Batch 1314/1582 -  Discriminator loss: 0.3807 -  Generator loss: 4.0772 - 
Epoch 1/1 -  Batch 1315/1582 -  Discriminator loss: 0.3841 -  Generator loss: 3.0340 - 
Epoch 1/1 -  Batch 1316/1582 -  Discriminator loss: 0.3787 -  Generator loss: 4.1552 - 
Epoch 1/1 -  Batch 1317/1582 -  Discriminator loss: 0.3710 -  Generator loss: 3.3405 - 
Epoch 1/1 -  Batch 1318/1582 -  Discriminator loss: 0.3713 -  Generator loss: 3.4073 - 
Epoch 1/1 -  Batch 1319/1582 -  Discriminator loss: 0.3653 -  Generator loss: 3.5903 - 
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Epoch 1/1 -  Batch 1327/1582 -  Discriminator loss: 0.3770 -  Generator loss: 3.2312 - 
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Epoch 1/1 -  Batch 1329/1582 -  Discriminator loss: 0.3577 -  Generator loss: 3.8779 - 
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Epoch 1/1 -  Batch 1331/1582 -  Discriminator loss: 0.3417 -  Generator loss: 4.4950 - 
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Epoch 1/1 -  Batch 1333/1582 -  Discriminator loss: 0.3770 -  Generator loss: 4.6658 - 
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Epoch 1/1 -  Batch 1338/1582 -  Discriminator loss: 0.3606 -  Generator loss: 4.8988 - 
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Epoch 1/1 -  Batch 1341/1582 -  Discriminator loss: 0.3709 -  Generator loss: 3.3637 - 
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Epoch 1/1 -  Batch 1343/1582 -  Discriminator loss: 0.3601 -  Generator loss: 3.6687 - 
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Epoch 1/1 -  Batch 1346/1582 -  Discriminator loss: 0.3661 -  Generator loss: 3.5349 - 
Epoch 1/1 -  Batch 1347/1582 -  Discriminator loss: 0.3554 -  Generator loss: 4.3425 - 
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Epoch 1/1 -  Batch 1355/1582 -  Discriminator loss: 0.3581 -  Generator loss: 3.6714 - 
Epoch 1/1 -  Batch 1356/1582 -  Discriminator loss: 0.3651 -  Generator loss: 3.4261 - 
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Epoch 1/1 -  Batch 1358/1582 -  Discriminator loss: 0.3682 -  Generator loss: 3.4949 - 
Epoch 1/1 -  Batch 1359/1582 -  Discriminator loss: 0.3568 -  Generator loss: 4.2924 - 
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Epoch 1/1 -  Batch 1363/1582 -  Discriminator loss: 0.3621 -  Generator loss: 4.0770 - 
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Epoch 1/1 -  Batch 1365/1582 -  Discriminator loss: 0.3637 -  Generator loss: 3.4861 - 
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Epoch 1/1 -  Batch 1367/1582 -  Discriminator loss: 0.3583 -  Generator loss: 3.7258 - 
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Epoch 1/1 -  Batch 1371/1582 -  Discriminator loss: 0.3538 -  Generator loss: 3.9044 - 
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Epoch 1/1 -  Batch 1376/1582 -  Discriminator loss: 0.3607 -  Generator loss: 4.1144 - 
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Epoch 1/1 -  Batch 1378/1582 -  Discriminator loss: 0.3695 -  Generator loss: 4.1482 - 
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Epoch 1/1 -  Batch 1383/1582 -  Discriminator loss: 0.3721 -  Generator loss: 3.3147 - 
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Epoch 1/1 -  Batch 1390/1582 -  Discriminator loss: 0.3605 -  Generator loss: 3.7244 - 
Epoch 1/1 -  Batch 1391/1582 -  Discriminator loss: 0.3517 -  Generator loss: 4.0406 - 
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Epoch 1/1 -  Batch 1407/1582 -  Discriminator loss: 0.3636 -  Generator loss: 4.9451 - 
Epoch 1/1 -  Batch 1408/1582 -  Discriminator loss: 0.4177 -  Generator loss: 2.7901 - 
Epoch 1/1 -  Batch 1409/1582 -  Discriminator loss: 0.5652 -  Generator loss: 6.4379 - 
Epoch 1/1 -  Batch 1410/1582 -  Discriminator loss: 0.4276 -  Generator loss: 4.7907 - 
Epoch 1/1 -  Batch 1411/1582 -  Discriminator loss: 0.4766 -  Generator loss: 2.3190 - 
Epoch 1/1 -  Batch 1412/1582 -  Discriminator loss: 0.4834 -  Generator loss: 5.0028 - 
Epoch 1/1 -  Batch 1413/1582 -  Discriminator loss: 0.4048 -  Generator loss: 3.9046 - 
Epoch 1/1 -  Batch 1414/1582 -  Discriminator loss: 0.4343 -  Generator loss: 2.4662 - 
Epoch 1/1 -  Batch 1415/1582 -  Discriminator loss: 0.3979 -  Generator loss: 3.8277 - 
Epoch 1/1 -  Batch 1416/1582 -  Discriminator loss: 0.3836 -  Generator loss: 3.3213 - 
Epoch 1/1 -  Batch 1417/1582 -  Discriminator loss: 0.3905 -  Generator loss: 2.9755 - 
Epoch 1/1 -  Batch 1418/1582 -  Discriminator loss: 0.3783 -  Generator loss: 3.5254 - 
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Epoch 1/1 -  Batch 1420/1582 -  Discriminator loss: 0.3705 -  Generator loss: 3.3158 - 
Epoch 1/1 -  Batch 1421/1582 -  Discriminator loss: 0.3728 -  Generator loss: 4.1120 - 
Epoch 1/1 -  Batch 1422/1582 -  Discriminator loss: 0.3675 -  Generator loss: 3.4107 - 
Epoch 1/1 -  Batch 1423/1582 -  Discriminator loss: 0.3598 -  Generator loss: 3.8095 - 
Epoch 1/1 -  Batch 1424/1582 -  Discriminator loss: 0.3698 -  Generator loss: 3.3495 - 
Epoch 1/1 -  Batch 1425/1582 -  Discriminator loss: 0.3748 -  Generator loss: 3.4652 - 
Epoch 1/1 -  Batch 1426/1582 -  Discriminator loss: 0.3673 -  Generator loss: 3.5458 - 
Epoch 1/1 -  Batch 1427/1582 -  Discriminator loss: 0.3678 -  Generator loss: 3.5843 - 
Epoch 1/1 -  Batch 1428/1582 -  Discriminator loss: 0.3717 -  Generator loss: 3.3604 - 
Epoch 1/1 -  Batch 1429/1582 -  Discriminator loss: 0.3704 -  Generator loss: 3.5438 - 
Epoch 1/1 -  Batch 1430/1582 -  Discriminator loss: 0.3716 -  Generator loss: 3.3492 - 
Epoch 1/1 -  Batch 1431/1582 -  Discriminator loss: 0.3717 -  Generator loss: 3.3573 - 
Epoch 1/1 -  Batch 1432/1582 -  Discriminator loss: 0.3748 -  Generator loss: 3.2915 - 
Epoch 1/1 -  Batch 1433/1582 -  Discriminator loss: 0.3720 -  Generator loss: 3.6597 - 
Epoch 1/1 -  Batch 1434/1582 -  Discriminator loss: 0.3747 -  Generator loss: 3.2259 - 
Epoch 1/1 -  Batch 1435/1582 -  Discriminator loss: 0.3707 -  Generator loss: 3.4630 - 
Epoch 1/1 -  Batch 1436/1582 -  Discriminator loss: 0.3625 -  Generator loss: 3.9326 - 
Epoch 1/1 -  Batch 1437/1582 -  Discriminator loss: 0.3741 -  Generator loss: 3.1929 - 
Epoch 1/1 -  Batch 1438/1582 -  Discriminator loss: 0.3737 -  Generator loss: 3.8693 - 
Epoch 1/1 -  Batch 1439/1582 -  Discriminator loss: 0.3668 -  Generator loss: 3.4954 - 
Epoch 1/1 -  Batch 1440/1582 -  Discriminator loss: 0.3584 -  Generator loss: 3.7695 - 
Epoch 1/1 -  Batch 1441/1582 -  Discriminator loss: 0.3512 -  Generator loss: 3.9869 - 
Epoch 1/1 -  Batch 1442/1582 -  Discriminator loss: 0.3657 -  Generator loss: 3.4577 - 
Epoch 1/1 -  Batch 1443/1582 -  Discriminator loss: 0.3637 -  Generator loss: 4.0421 - 
Epoch 1/1 -  Batch 1444/1582 -  Discriminator loss: 0.3625 -  Generator loss: 3.5231 - 
Epoch 1/1 -  Batch 1445/1582 -  Discriminator loss: 0.3581 -  Generator loss: 4.0651 - 
Epoch 1/1 -  Batch 1446/1582 -  Discriminator loss: 0.3568 -  Generator loss: 3.7826 - 
Epoch 1/1 -  Batch 1447/1582 -  Discriminator loss: 0.3572 -  Generator loss: 3.9467 - 
Epoch 1/1 -  Batch 1448/1582 -  Discriminator loss: 0.3544 -  Generator loss: 3.9455 - 
Epoch 1/1 -  Batch 1449/1582 -  Discriminator loss: 0.3499 -  Generator loss: 4.0939 - 
Epoch 1/1 -  Batch 1450/1582 -  Discriminator loss: 0.3548 -  Generator loss: 3.8103 - 
Epoch 1/1 -  Batch 1451/1582 -  Discriminator loss: 0.3594 -  Generator loss: 3.9845 - 
Epoch 1/1 -  Batch 1452/1582 -  Discriminator loss: 0.3774 -  Generator loss: 3.1719 - 
Epoch 1/1 -  Batch 1453/1582 -  Discriminator loss: 0.3716 -  Generator loss: 3.4849 - 
Epoch 1/1 -  Batch 1454/1582 -  Discriminator loss: 0.3651 -  Generator loss: 3.5992 - 
Epoch 1/1 -  Batch 1455/1582 -  Discriminator loss: 0.3645 -  Generator loss: 3.4798 - 
Epoch 1/1 -  Batch 1456/1582 -  Discriminator loss: 0.3663 -  Generator loss: 4.0628 - 
Epoch 1/1 -  Batch 1457/1582 -  Discriminator loss: 0.3708 -  Generator loss: 3.2927 - 
Epoch 1/1 -  Batch 1458/1582 -  Discriminator loss: 0.3678 -  Generator loss: 4.0904 - 
Epoch 1/1 -  Batch 1459/1582 -  Discriminator loss: 0.3712 -  Generator loss: 3.3301 - 
Epoch 1/1 -  Batch 1460/1582 -  Discriminator loss: 0.3713 -  Generator loss: 4.2544 - 
Epoch 1/1 -  Batch 1461/1582 -  Discriminator loss: 0.3678 -  Generator loss: 3.3966 - 
Epoch 1/1 -  Batch 1462/1582 -  Discriminator loss: 0.3680 -  Generator loss: 3.4101 - 
Epoch 1/1 -  Batch 1463/1582 -  Discriminator loss: 0.3631 -  Generator loss: 4.2044 - 
Epoch 1/1 -  Batch 1464/1582 -  Discriminator loss: 0.3701 -  Generator loss: 3.3123 - 
Epoch 1/1 -  Batch 1465/1582 -  Discriminator loss: 0.3637 -  Generator loss: 4.4603 - 
Epoch 1/1 -  Batch 1466/1582 -  Discriminator loss: 0.3695 -  Generator loss: 3.3415 - 
Epoch 1/1 -  Batch 1467/1582 -  Discriminator loss: 0.3693 -  Generator loss: 4.5254 - 
Epoch 1/1 -  Batch 1468/1582 -  Discriminator loss: 0.3641 -  Generator loss: 3.5021 - 
Epoch 1/1 -  Batch 1469/1582 -  Discriminator loss: 0.3581 -  Generator loss: 4.2191 - 
Epoch 1/1 -  Batch 1470/1582 -  Discriminator loss: 0.3581 -  Generator loss: 3.6732 - 
Epoch 1/1 -  Batch 1471/1582 -  Discriminator loss: 0.3602 -  Generator loss: 4.0989 - 
Epoch 1/1 -  Batch 1472/1582 -  Discriminator loss: 0.3659 -  Generator loss: 3.4568 - 
Epoch 1/1 -  Batch 1473/1582 -  Discriminator loss: 0.3595 -  Generator loss: 4.2038 - 
Epoch 1/1 -  Batch 1474/1582 -  Discriminator loss: 0.3650 -  Generator loss: 3.4942 - 
Epoch 1/1 -  Batch 1475/1582 -  Discriminator loss: 0.3687 -  Generator loss: 3.9959 - 
Epoch 1/1 -  Batch 1476/1582 -  Discriminator loss: 0.3560 -  Generator loss: 3.7899 - 
Epoch 1/1 -  Batch 1477/1582 -  Discriminator loss: 0.3540 -  Generator loss: 3.9384 - 
Epoch 1/1 -  Batch 1478/1582 -  Discriminator loss: 0.3641 -  Generator loss: 3.4758 - 
Epoch 1/1 -  Batch 1479/1582 -  Discriminator loss: 0.3688 -  Generator loss: 4.0255 - 
Epoch 1/1 -  Batch 1480/1582 -  Discriminator loss: 0.3581 -  Generator loss: 3.6205 - 
Epoch 1/1 -  Batch 1481/1582 -  Discriminator loss: 0.3545 -  Generator loss: 4.0047 - 
Epoch 1/1 -  Batch 1482/1582 -  Discriminator loss: 0.3569 -  Generator loss: 3.8135 - 
Epoch 1/1 -  Batch 1483/1582 -  Discriminator loss: 0.3581 -  Generator loss: 3.8389 - 
Epoch 1/1 -  Batch 1484/1582 -  Discriminator loss: 0.3533 -  Generator loss: 3.9595 - 
Epoch 1/1 -  Batch 1485/1582 -  Discriminator loss: 0.3501 -  Generator loss: 4.0011 - 
Epoch 1/1 -  Batch 1486/1582 -  Discriminator loss: 0.3561 -  Generator loss: 4.1584 - 
Epoch 1/1 -  Batch 1487/1582 -  Discriminator loss: 0.3557 -  Generator loss: 3.7765 - 
Epoch 1/1 -  Batch 1488/1582 -  Discriminator loss: 0.3547 -  Generator loss: 4.2782 - 
Epoch 1/1 -  Batch 1489/1582 -  Discriminator loss: 0.3573 -  Generator loss: 3.7090 - 
Epoch 1/1 -  Batch 1490/1582 -  Discriminator loss: 0.3586 -  Generator loss: 4.1472 - 
Epoch 1/1 -  Batch 1491/1582 -  Discriminator loss: 0.3528 -  Generator loss: 3.9259 - 
Epoch 1/1 -  Batch 1492/1582 -  Discriminator loss: 0.3484 -  Generator loss: 4.1459 - 
Epoch 1/1 -  Batch 1493/1582 -  Discriminator loss: 0.3536 -  Generator loss: 3.9800 - 
Epoch 1/1 -  Batch 1494/1582 -  Discriminator loss: 0.3560 -  Generator loss: 3.7372 - 
Epoch 1/1 -  Batch 1495/1582 -  Discriminator loss: 0.3536 -  Generator loss: 4.0741 - 
Epoch 1/1 -  Batch 1496/1582 -  Discriminator loss: 0.3492 -  Generator loss: 4.1765 - 
Epoch 1/1 -  Batch 1497/1582 -  Discriminator loss: 0.3476 -  Generator loss: 4.1260 - 
Epoch 1/1 -  Batch 1498/1582 -  Discriminator loss: 0.3525 -  Generator loss: 4.1532 - 
Epoch 1/1 -  Batch 1499/1582 -  Discriminator loss: 0.3582 -  Generator loss: 3.6273 - 
Epoch 1/1 -  Batch 1500/1582 -  Discriminator loss: 0.3555 -  Generator loss: 4.2259 - 
Epoch 1/1 -  Batch 1501/1582 -  Discriminator loss: 0.3482 -  Generator loss: 4.0740 - 
Epoch 1/1 -  Batch 1502/1582 -  Discriminator loss: 0.3670 -  Generator loss: 3.4016 - 
Epoch 1/1 -  Batch 1503/1582 -  Discriminator loss: 0.3862 -  Generator loss: 4.5718 - 
Epoch 1/1 -  Batch 1504/1582 -  Discriminator loss: 0.3883 -  Generator loss: 3.1110 - 
Epoch 1/1 -  Batch 1505/1582 -  Discriminator loss: 0.4539 -  Generator loss: 5.5023 - 
Epoch 1/1 -  Batch 1506/1582 -  Discriminator loss: 0.3722 -  Generator loss: 3.4090 - 
Epoch 1/1 -  Batch 1507/1582 -  Discriminator loss: 0.3917 -  Generator loss: 2.9409 - 
Epoch 1/1 -  Batch 1508/1582 -  Discriminator loss: 0.4141 -  Generator loss: 4.6439 - 
Epoch 1/1 -  Batch 1509/1582 -  Discriminator loss: 0.3714 -  Generator loss: 3.4140 - 
Epoch 1/1 -  Batch 1510/1582 -  Discriminator loss: 0.3641 -  Generator loss: 4.1547 - 
Epoch 1/1 -  Batch 1511/1582 -  Discriminator loss: 0.3706 -  Generator loss: 3.3047 - 
Epoch 1/1 -  Batch 1512/1582 -  Discriminator loss: 0.3698 -  Generator loss: 3.5043 - 
Epoch 1/1 -  Batch 1513/1582 -  Discriminator loss: 0.3604 -  Generator loss: 3.6656 - 
Epoch 1/1 -  Batch 1514/1582 -  Discriminator loss: 0.3592 -  Generator loss: 3.6409 - 
Epoch 1/1 -  Batch 1515/1582 -  Discriminator loss: 0.3701 -  Generator loss: 5.0567 - 
Epoch 1/1 -  Batch 1516/1582 -  Discriminator loss: 0.3784 -  Generator loss: 3.2812 - 
Epoch 1/1 -  Batch 1517/1582 -  Discriminator loss: 0.3845 -  Generator loss: 4.7154 - 
Epoch 1/1 -  Batch 1518/1582 -  Discriminator loss: 0.3629 -  Generator loss: 3.5206 - 
Epoch 1/1 -  Batch 1519/1582 -  Discriminator loss: 0.3526 -  Generator loss: 4.1694 - 
Epoch 1/1 -  Batch 1520/1582 -  Discriminator loss: 0.3552 -  Generator loss: 3.8907 - 
Epoch 1/1 -  Batch 1521/1582 -  Discriminator loss: 0.3701 -  Generator loss: 3.3460 - 
Epoch 1/1 -  Batch 1522/1582 -  Discriminator loss: 0.3701 -  Generator loss: 5.2072 - 
Epoch 1/1 -  Batch 1523/1582 -  Discriminator loss: 0.3647 -  Generator loss: 3.6286 - 
Epoch 1/1 -  Batch 1524/1582 -  Discriminator loss: 0.3612 -  Generator loss: 4.6675 - 
Epoch 1/1 -  Batch 1525/1582 -  Discriminator loss: 0.3810 -  Generator loss: 3.1191 - 
Epoch 1/1 -  Batch 1526/1582 -  Discriminator loss: 0.3853 -  Generator loss: 4.6297 - 
Epoch 1/1 -  Batch 1527/1582 -  Discriminator loss: 0.3708 -  Generator loss: 3.3682 - 
Epoch 1/1 -  Batch 1528/1582 -  Discriminator loss: 0.3599 -  Generator loss: 3.6686 - 
Epoch 1/1 -  Batch 1529/1582 -  Discriminator loss: 0.3680 -  Generator loss: 3.5849 - 
Epoch 1/1 -  Batch 1530/1582 -  Discriminator loss: 0.3521 -  Generator loss: 4.1893 - 
Epoch 1/1 -  Batch 1531/1582 -  Discriminator loss: 0.3783 -  Generator loss: 3.1297 - 
Epoch 1/1 -  Batch 1532/1582 -  Discriminator loss: 0.3718 -  Generator loss: 4.2916 - 
Epoch 1/1 -  Batch 1533/1582 -  Discriminator loss: 0.3618 -  Generator loss: 3.5290 - 
Epoch 1/1 -  Batch 1534/1582 -  Discriminator loss: 0.3686 -  Generator loss: 3.3363 - 
Epoch 1/1 -  Batch 1535/1582 -  Discriminator loss: 0.3692 -  Generator loss: 3.9434 - 
Epoch 1/1 -  Batch 1536/1582 -  Discriminator loss: 0.3613 -  Generator loss: 3.6399 - 
Epoch 1/1 -  Batch 1537/1582 -  Discriminator loss: 0.3625 -  Generator loss: 3.5180 - 
Epoch 1/1 -  Batch 1538/1582 -  Discriminator loss: 0.3649 -  Generator loss: 4.2166 - 
Epoch 1/1 -  Batch 1539/1582 -  Discriminator loss: 0.3717 -  Generator loss: 3.3095 - 
Epoch 1/1 -  Batch 1540/1582 -  Discriminator loss: 0.3611 -  Generator loss: 3.9031 - 
Epoch 1/1 -  Batch 1541/1582 -  Discriminator loss: 0.3753 -  Generator loss: 3.1717 - 
Epoch 1/1 -  Batch 1542/1582 -  Discriminator loss: 0.3881 -  Generator loss: 4.3548 - 
Epoch 1/1 -  Batch 1543/1582 -  Discriminator loss: 0.3856 -  Generator loss: 3.0316 - 
Epoch 1/1 -  Batch 1544/1582 -  Discriminator loss: 0.3701 -  Generator loss: 3.5239 - 
Epoch 1/1 -  Batch 1545/1582 -  Discriminator loss: 0.3724 -  Generator loss: 3.3497 - 
Epoch 1/1 -  Batch 1546/1582 -  Discriminator loss: 0.3588 -  Generator loss: 3.9294 - 
Epoch 1/1 -  Batch 1547/1582 -  Discriminator loss: 0.3702 -  Generator loss: 3.3374 - 
Epoch 1/1 -  Batch 1548/1582 -  Discriminator loss: 0.3631 -  Generator loss: 3.6883 - 
Epoch 1/1 -  Batch 1549/1582 -  Discriminator loss: 0.3614 -  Generator loss: 3.6966 - 
Epoch 1/1 -  Batch 1550/1582 -  Discriminator loss: 0.3563 -  Generator loss: 4.6207 - 
Epoch 1/1 -  Batch 1551/1582 -  Discriminator loss: 0.3730 -  Generator loss: 3.3062 - 
Epoch 1/1 -  Batch 1552/1582 -  Discriminator loss: 0.3658 -  Generator loss: 4.6785 - 
Epoch 1/1 -  Batch 1553/1582 -  Discriminator loss: 0.3680 -  Generator loss: 3.4557 - 
Epoch 1/1 -  Batch 1554/1582 -  Discriminator loss: 0.3922 -  Generator loss: 5.1670 - 
Epoch 1/1 -  Batch 1555/1582 -  Discriminator loss: 0.3778 -  Generator loss: 3.2467 - 
Epoch 1/1 -  Batch 1556/1582 -  Discriminator loss: 0.3662 -  Generator loss: 3.5148 - 
Epoch 1/1 -  Batch 1557/1582 -  Discriminator loss: 0.3594 -  Generator loss: 4.0554 - 
Epoch 1/1 -  Batch 1558/1582 -  Discriminator loss: 0.3580 -  Generator loss: 3.6988 - 
Epoch 1/1 -  Batch 1559/1582 -  Discriminator loss: 0.3694 -  Generator loss: 3.3792 - 
Epoch 1/1 -  Batch 1560/1582 -  Discriminator loss: 0.3723 -  Generator loss: 4.1880 - 
Epoch 1/1 -  Batch 1561/1582 -  Discriminator loss: 0.3696 -  Generator loss: 3.3233 - 
Epoch 1/1 -  Batch 1562/1582 -  Discriminator loss: 0.3629 -  Generator loss: 3.7696 - 
Epoch 1/1 -  Batch 1563/1582 -  Discriminator loss: 0.3612 -  Generator loss: 3.8084 - 
Epoch 1/1 -  Batch 1564/1582 -  Discriminator loss: 0.3554 -  Generator loss: 3.8500 - 
Epoch 1/1 -  Batch 1565/1582 -  Discriminator loss: 0.3853 -  Generator loss: 3.0092 - 
Epoch 1/1 -  Batch 1566/1582 -  Discriminator loss: 0.4048 -  Generator loss: 4.5489 - 
Epoch 1/1 -  Batch 1567/1582 -  Discriminator loss: 0.3665 -  Generator loss: 3.4613 - 
Epoch 1/1 -  Batch 1568/1582 -  Discriminator loss: 0.3861 -  Generator loss: 2.9908 - 
Epoch 1/1 -  Batch 1569/1582 -  Discriminator loss: 0.3938 -  Generator loss: 4.2529 - 
Epoch 1/1 -  Batch 1570/1582 -  Discriminator loss: 0.3791 -  Generator loss: 3.1331 - 
Epoch 1/1 -  Batch 1571/1582 -  Discriminator loss: 0.3672 -  Generator loss: 3.5989 - 
Epoch 1/1 -  Batch 1572/1582 -  Discriminator loss: 0.3670 -  Generator loss: 3.5072 - 
Epoch 1/1 -  Batch 1573/1582 -  Discriminator loss: 0.3656 -  Generator loss: 3.5822 - 
Epoch 1/1 -  Batch 1574/1582 -  Discriminator loss: 0.3556 -  Generator loss: 3.7617 - 
Epoch 1/1 -  Batch 1575/1582 -  Discriminator loss: 0.3483 -  Generator loss: 4.5209 - 
Epoch 1/1 -  Batch 1576/1582 -  Discriminator loss: 0.3651 -  Generator loss: 3.4570 - 
Epoch 1/1 -  Batch 1577/1582 -  Discriminator loss: 0.3652 -  Generator loss: 4.2037 - 
Epoch 1/1 -  Batch 1578/1582 -  Discriminator loss: 0.3626 -  Generator loss: 3.5241 - 
Epoch 1/1 -  Batch 1579/1582 -  Discriminator loss: 0.3516 -  Generator loss: 4.2187 - 
Epoch 1/1 -  Batch 1580/1582 -  Discriminator loss: 0.3580 -  Generator loss: 3.7486 - 
Epoch 1/1 -  Batch 1581/1582 -  Discriminator loss: 0.3494 -  Generator loss: 4.4394 - 
Epoch 1/1 -  Batch 1582/1582 -  Discriminator loss: 0.3830 -  Generator loss: 3.0611 - 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.